Download Text Processing with GATE (Version 6)

Developing Language Processing
Components with GATE
Version 6 (a User Guide)
For GATE version 6.1
(built April 21, 2011)
Hamish Cunningham
Diana Maynard
Kalina Bontcheva
Valentin Tablan
Niraj Aswani
Ian Roberts
Genevieve Gorrell
Adam Funk
Angus Roberts
Danica Damljanovic
Thomas Heitz
Mark A. Greenwood
Horacio Saggion
Johann Petrak
Yaoyong Li
Wim Peters
et al
©The University of Sheffield, Department of Computer Science 2001-2011
http://gate.ac.uk/
This user manual is free, but please consider making a donation.
HTML version: http://gate.ac.uk/userguide
Work on GATE has been partly supported by EPSRC grants GR/K25267 (Large-Scale
Information Extraction), GR/M31699 (GATE 2), RA007940 (EMILLE), GR/N15764/01 (AKT)
and GR/R85150/01 (MIAKT), AHRB grant APN16396 (ETCSL/GATE), Matrixware, the
Information Retrieval Facility and several EU-funded projects: (SEKT, TAO, NeOn,
MediaCampaign, Musing, KnowledgeWeb, PrestoSpace, h-TechSight, and enIRaF).
Developing Language Processing Components with GATE Version 6.0
©2011 The University of Sheffield, Department of Computer Science
The University of Sheffield, Department of Computer Science
Regent Court
211 Portobello
Sheffield
S1 4DP
United Kingdom
http://gate.ac.uk
This work is licenced under the Creative Commons Attribution-No Derivative Licence. You are
free to copy, distribute, display, and perform the work under the following conditions:
• Attribution You must give the original author credit.
• No Derivative Works You may not alter, transform, or build upon this work.
With the understanding that:
• Waiver Any of the above conditions can be waived if you get permission from the copyright
holder.
• Other Rights In no way are any of the following rights affected by the license: your fair
dealing or fair use rights; the author’s moral rights; rights other persons may have either in
the work itself or in how the work is used, such as publicity or privacy rights.
• Notice For any reuse or distribution, you must make clear to others the licence terms of
this work.
For more information about the Creative Commons Attribution-No Derivative License, please visit
this web address: http://creativecommons.org/licenses/by-nd/2.0/uk/
Brief Contents
I
GATE Basics
3
1 Introduction
5
2 Installing and Running GATE
27
3 Using GATE Developer
39
4 CREOLE: the GATE Component Model
69
5 Language Resources: Corpora, Documents and Annotations
89
6 ANNIE: a Nearly-New Information Extraction System
II
GATE for Advanced Users
113
131
7 GATE Embedded
133
8 JAPE: Regular Expressions over Annotations
181
9 ANNIC: ANNotations-In-Context
217
10 Performance Evaluation of Language Analysers
227
11 Profiling Processing Resources
257
12 Developing GATE
265
III
CREOLE Plugins
277
13 Gazetteers
279
14 Working with Ontologies
301
15 Non-English Language Support
339
16 Parsers
345
17 Machine Learning
359
18 Tools for Alignment Tasks
409
iii
iv
Contents
19 Combining GATE and UIMA
425
20 More (CREOLE) Plugins
437
IV
The GATE Family: Cloud, MIMIR, Teamware
21 GATE Cloud
497
499
22 GATE Teamware: A Web-based Collaborative Corpus Annotation Tool 509
23 GATE Mı́mir
523
Appendices
523
A Change Log
525
B Version 5.1 Plugins Name Map
553
C Design Notes
555
D JAPE: Implementation
563
E Ant Tasks for GATE
573
F Named-Entity State Machine Patterns
581
G Part-of-Speech Tags used in the Hepple Tagger
589
References
591
Contents
I
GATE Basics
1 Introduction
1.1 How to Use this Text . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.1 Developing and Deploying Language Processing Facilities
1.3.2 Built-In Components . . . . . . . . . . . . . . . . . . . .
1.3.3 Additional Facilities in GATE Developer/Embedded . .
1.3.4 An Example . . . . . . . . . . . . . . . . . . . . . . . . .
1.4 Some Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . .
1.5 Recent Changes . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.5.1 Version 6.1 (April 2011) . . . . . . . . . . . . . . . . . .
1.6 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . .
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2 Installing and Running GATE
2.1 Downloading GATE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Installing and Running GATE . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 The Easy Way . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2 The Hard Way (1) . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.3 The Hard Way (2): Subversion . . . . . . . . . . . . . . . . . . . . .
2.2.4 Running GATE Developer on Unix/Linux . . . . . . . . . . . . . . .
2.3 Using System Properties with GATE . . . . . . . . . . . . . . . . . . . . . .
2.4 Configuring GATE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.5 Building GATE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.5.1 Using GATE with Maven/Ivy . . . . . . . . . . . . . . . . . . . . . .
2.6 Uninstalling GATE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7 Troubleshooting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7.1 I don’t see the Java console messages under Windows . . . . . . . . .
2.7.2 When I execute GATE, nothing happens . . . . . . . . . . . . . . . .
2.7.3 On Ubuntu, GATE is very slow or doesn’t start . . . . . . . . . . . .
2.7.4 How to use GATE on a 64 bit system? . . . . . . . . . . . . . . . . .
2.7.5 I got the error: Could not reserve enough space for object heap . . .
2.7.6 From Eclipse, I got the error: java.lang.OutOfMemoryError: Java
heap space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Contents
2.7.7
On MacOS, I got the error: java.lang.OutOfMemoryError: Java heap
space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7.8 I got the error: log4j:WARN No appenders could be found for logger...
2.7.9 Text is incorrectly refreshed after scrolling and become unreadable . .
2.7.10 An error occurred when running the TreeTagger plugin . . . . . . . .
2.7.11 I got the error: HighlightData cannot be cast to ...HighlightInfo . . .
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3 Using GATE Developer
3.1 The GATE Developer Main Window . . . . . . . . . . . . . . . . . . .
3.2 Loading and Viewing Documents . . . . . . . . . . . . . . . . . . . . .
3.3 Creating and Viewing Corpora . . . . . . . . . . . . . . . . . . . . . . .
3.4 Working with Annotations . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.1 The Annotation Sets View . . . . . . . . . . . . . . . . . . . . .
3.4.2 The Annotations List View . . . . . . . . . . . . . . . . . . . .
3.4.3 The Annotations Stack View . . . . . . . . . . . . . . . . . . . .
3.4.4 The Co-reference Editor . . . . . . . . . . . . . . . . . . . . . .
3.4.5 Creating and Editing Annotations . . . . . . . . . . . . . . . . .
3.4.6 Schema-Driven Editing . . . . . . . . . . . . . . . . . . . . . . .
3.4.7 Printing Text with Annotations . . . . . . . . . . . . . . . . . .
3.5 Using CREOLE Plugins . . . . . . . . . . . . . . . . . . . . . . . . . .
3.6 Loading and Using Processing Resources . . . . . . . . . . . . . . . . .
3.7 Creating and Running an Application . . . . . . . . . . . . . . . . . . .
3.7.1 Running an Application on a Datastore . . . . . . . . . . . . . .
3.7.2 Running PRs Conditionally on Document Features . . . . . . .
3.7.3 Doing Information Extraction with ANNIE . . . . . . . . . . . .
3.7.4 Modifying ANNIE . . . . . . . . . . . . . . . . . . . . . . . . .
3.8 Saving Applications and Language Resources . . . . . . . . . . . . . . .
3.8.1 Saving Documents to File . . . . . . . . . . . . . . . . . . . . .
3.8.2 Saving and Restoring LRs in Datastores . . . . . . . . . . . . .
3.8.3 Saving Application States to a File . . . . . . . . . . . . . . . .
3.8.4 Saving an Application with its Resources (e.g. GATECloud.net)
3.9 Keyboard Shortcuts . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.10 Miscellaneous . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.10.1 Stopping GATE from Restoring Developer Sessions/Options . .
3.10.2 Working with Unicode . . . . . . . . . . . . . . . . . . . . . . .
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4 CREOLE: the GATE Component Model
4.1 The Web and CREOLE . . . . . . . . .
4.2 The GATE Framework . . . . . . . . . .
4.3 The Lifecycle of a CREOLE Resource . .
4.4 Processing Resources and Applications .
4.5 Language Resources and Datastores . . .
4.6 Built-in CREOLE Resources . . . . . . .
4.7 CREOLE Resource Configuration . . . .
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Contents
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5 Language Resources: Corpora, Documents and Annotations
5.1 Features: Simple Attribute/Value Data . . . . . . . . . . . . . .
5.2 Corpora: Sets of Documents plus Features . . . . . . . . . . . .
5.3 Documents: Content plus Annotations plus Features . . . . . .
5.4 Annotations: Directed Acyclic Graphs . . . . . . . . . . . . . .
5.4.1 Annotation Schemas . . . . . . . . . . . . . . . . . . . .
5.4.2 Examples of Annotated Documents . . . . . . . . . . . .
5.4.3 Creating, Viewing and Editing Diverse Annotation Types
5.5 Document Formats . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.1 Detecting the Right Reader . . . . . . . . . . . . . . . .
5.5.2 XML . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.3 HTML . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.4 SGML . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.5 Plain text . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.6 RTF . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.7 Email . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.8 PDF Files and Office Documents . . . . . . . . . . . . .
5.6 XML Input/Output . . . . . . . . . . . . . . . . . . . . . . . . .
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4.8
4.7.1
4.7.2
4.7.3
Tools:
4.8.1
vii
Configuration with XML . . . . . . . . .
Configuring Resources using Annotations
Mixing the Configuration Styles . . . . .
How to Add Utilities to GATE Developer
Putting your tools in a sub-menu . . . .
6 ANNIE: a Nearly-New Information Extraction
6.1 Document Reset . . . . . . . . . . . . . . . . . .
6.2 Tokeniser . . . . . . . . . . . . . . . . . . . . .
6.2.1 Tokeniser Rules . . . . . . . . . . . . . .
6.2.2 Token Types . . . . . . . . . . . . . . .
6.2.3 English Tokeniser . . . . . . . . . . . . .
6.3 Gazetteer . . . . . . . . . . . . . . . . . . . . .
6.4 Sentence Splitter . . . . . . . . . . . . . . . . .
6.5 RegEx Sentence Splitter . . . . . . . . . . . . .
6.6 Part of Speech Tagger . . . . . . . . . . . . . .
6.7 Semantic Tagger . . . . . . . . . . . . . . . . .
6.8 Orthographic Coreference (OrthoMatcher) . . .
6.8.1 GATE Interface . . . . . . . . . . . . . .
6.8.2 Resources . . . . . . . . . . . . . . . . .
6.8.3 Processing . . . . . . . . . . . . . . . . .
6.9 Pronominal Coreference . . . . . . . . . . . . .
6.9.1 Quoted Speech Submodule . . . . . . . .
6.9.2 Pleonastic It Submodule . . . . . . . . .
6.9.3 Pronominal Resolution Submodule . . .
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viii
Contents
6.9.4 Detailed Description of the
6.10 A Walk-Through Example . . . .
6.10.1 Step 1 - Tokenisation . . .
6.10.2 Step 2 - List Lookup . . .
6.10.3 Step 3 - Grammar Rules .
II
Algorithm .
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GATE for Advanced Users
7 GATE Embedded
7.1 Quick Start with GATE Embedded . . . . . . . . . . . . .
7.2 Resource Management in GATE Embedded . . . . . . . .
7.3 Using CREOLE Plugins . . . . . . . . . . . . . . . . . . .
7.4 Language Resources . . . . . . . . . . . . . . . . . . . . .
7.4.1 GATE Documents . . . . . . . . . . . . . . . . . .
7.4.2 Feature Maps . . . . . . . . . . . . . . . . . . . . .
7.4.3 Annotation Sets . . . . . . . . . . . . . . . . . . . .
7.4.4 Annotations . . . . . . . . . . . . . . . . . . . . . .
7.4.5 GATE Corpora . . . . . . . . . . . . . . . . . . . .
7.5 Processing Resources . . . . . . . . . . . . . . . . . . . . .
7.6 Controllers . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.7 Duplicating a Resource . . . . . . . . . . . . . . . . . . . .
7.8 Persistent Applications . . . . . . . . . . . . . . . . . . . .
7.9 Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.10 Creating a New Annotation Schema . . . . . . . . . . . . .
7.11 Creating a New CREOLE Resource . . . . . . . . . . . . .
7.12 Adding Support for a New Document Format . . . . . . .
7.13 Using GATE Embedded in a Multithreaded Environment .
7.14 Using GATE Embedded within a Spring Application . . .
7.14.1 Duplication in Spring . . . . . . . . . . . . . . . . .
7.14.2 Spring pooling . . . . . . . . . . . . . . . . . . . . .
7.14.3 Further reading . . . . . . . . . . . . . . . . . . . .
7.15 Using GATE Embedded within a Tomcat Web Application
7.15.1 Recommended Directory Structure . . . . . . . . .
7.15.2 Configuration Files . . . . . . . . . . . . . . . . . .
7.15.3 Initialization Code . . . . . . . . . . . . . . . . . .
7.16 Groovy for GATE . . . . . . . . . . . . . . . . . . . . . . .
7.16.1 Groovy Scripting Console for GATE . . . . . . . .
7.16.2 Groovy scripting PR . . . . . . . . . . . . . . . . .
7.16.3 The Scriptable Controller . . . . . . . . . . . . . .
7.16.4 Utility methods . . . . . . . . . . . . . . . . . . . .
7.17 Saving Config Data to gate.xml . . . . . . . . . . . . . . .
7.18 Annotation merging through the API . . . . . . . . . . . .
8 JAPE: Regular Expressions over Annotations
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Contents
ix
8.1
The Left-Hand Side . . . . . . . . . . . . . . . . . . . . .
8.1.1 Matching a Simple Text String . . . . . . . . . .
8.1.2 Matching Entire Annotation Types . . . . . . . .
8.1.3 Using Attributes and Values . . . . . . . . . . . .
8.1.4 Using Meta-Properties . . . . . . . . . . . . . . .
8.1.5 Using Templates . . . . . . . . . . . . . . . . . .
8.1.6 Multiple Pattern/Action Pairs . . . . . . . . . . .
8.1.7 LHS Macros . . . . . . . . . . . . . . . . . . . . .
8.1.8 Using Context . . . . . . . . . . . . . . . . . . . .
8.1.9 Multi-Constraint Statements . . . . . . . . . . . .
8.1.10 Negation . . . . . . . . . . . . . . . . . . . . . . .
8.1.11 Escaping Special Characters . . . . . . . . . . . .
8.2 LHS Operators in Detail . . . . . . . . . . . . . . . . . .
8.2.1 Compositional Operators . . . . . . . . . . . . . .
8.2.2 Matching Operators . . . . . . . . . . . . . . . .
8.3 The Right-Hand Side . . . . . . . . . . . . . . . . . . . .
8.3.1 A Simple Example . . . . . . . . . . . . . . . . .
8.3.2 Copying Feature Values from the LHS to the RHS
8.3.3 RHS Macros . . . . . . . . . . . . . . . . . . . . .
8.4 Use of Priority . . . . . . . . . . . . . . . . . . . . . . .
8.5 Using Phases Sequentially . . . . . . . . . . . . . . . . .
8.6 Using Java Code on the RHS . . . . . . . . . . . . . . .
8.6.1 A More Complex Example . . . . . . . . . . . . .
8.6.2 Adding a Feature to the Document . . . . . . . .
8.6.3 Finding the Tokens of a Matched Annotation . .
8.6.4 Using Named Blocks . . . . . . . . . . . . . . . .
8.6.5 Java RHS Overview . . . . . . . . . . . . . . . . .
8.7 Optimising for Speed . . . . . . . . . . . . . . . . . . . .
8.8 Ontology Aware Grammar Transduction . . . . . . . . .
8.9 Serializing JAPE Transducer . . . . . . . . . . . . . . . .
8.9.1 How to Serialize? . . . . . . . . . . . . . . . . . .
8.9.2 How to Use the Serialized Grammar File? . . . .
8.10 Notes for Montreal Transducer Users . . . . . . . . . . .
9 ANNIC: ANNotations-In-Context
9.1 Instantiating SSD . . . . . . . . . . . . . . . . .
9.2 Search GUI . . . . . . . . . . . . . . . . . . . .
9.2.1 Overview . . . . . . . . . . . . . . . . .
9.2.2 Syntax of Queries . . . . . . . . . . . . .
9.2.3 Top Section . . . . . . . . . . . . . . . .
9.2.4 Central Section . . . . . . . . . . . . . .
9.2.5 Bottom Section . . . . . . . . . . . . . .
9.3 Using SSD from GATE Embedded . . . . . . .
9.3.1 How to instantiate a searchabledatastore
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x
Contents
9.3.2
How to search in this datastore . . . . . . . . . . . . . . . . . . . . . 224
10 Performance Evaluation of Language Analysers
10.1 Metrics for Evaluation in Information Extraction . . . . . . . .
10.1.1 Annotation Relations . . . . . . . . . . . . . . . . . . . .
10.1.2 Cohen’s Kappa . . . . . . . . . . . . . . . . . . . . . . .
10.1.3 Precision, Recall, F-Measure . . . . . . . . . . . . . . . .
10.1.4 Macro and Micro Averaging . . . . . . . . . . . . . . . .
10.2 The Annotation Diff Tool . . . . . . . . . . . . . . . . . . . . .
10.2.1 Performing Evaluation with the Annotation Diff Tool . .
10.2.2 Creating a Gold Standard with the Annotation Diff Tool
10.3 Corpus Quality Assurance . . . . . . . . . . . . . . . . . . . . .
10.3.1 Description of the interface . . . . . . . . . . . . . . . . .
10.3.2 Step by step usage . . . . . . . . . . . . . . . . . . . . .
10.3.3 Details of the Corpus statistics table . . . . . . . . . . .
10.3.4 Details of the Document statistics table . . . . . . . . . .
10.3.5 GATE Embedded API for the measures . . . . . . . . .
10.3.6 sec:eval:qapr . . . . . . . . . . . . . . . . . . . . . . . . .
10.4 Corpus Benchmark Tool . . . . . . . . . . . . . . . . . . . . . .
10.4.1 Preparing the Corpora for Use . . . . . . . . . . . . . . .
10.4.2 Defining Properties . . . . . . . . . . . . . . . . . . . . .
10.4.3 Running the Tool . . . . . . . . . . . . . . . . . . . . . .
10.4.4 The Results . . . . . . . . . . . . . . . . . . . . . . . . .
10.5 A Plugin Computing Inter-Annotator Agreement (IAA) . . . . .
10.5.1 IAA for Classification . . . . . . . . . . . . . . . . . . . .
10.5.2 IAA For Named Entity Annotation . . . . . . . . . . . .
10.5.3 The BDM-Based IAA Scores . . . . . . . . . . . . . . . .
10.6 A Plugin Computing the BDM Scores for an Ontology . . . . .
10.7 Quality Assurance Summariser for Teamware . . . . . . . . . . .
11 Profiling Processing Resources
11.1 Overview . . . . . . . . . . . . . . .
11.1.1 Features . . . . . . . . . . .
11.1.2 Limitations . . . . . . . . .
11.2 Graphical User Interface . . . . . .
11.3 Command Line Interface . . . . . .
11.4 Application Programming Interface
11.4.1 Log4j.properties . . . . . . .
11.4.2 Benchmark log format . . .
11.4.3 Enabling profiling . . . . . .
11.4.4 Reporting tool . . . . . . .
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12 Developing GATE
265
12.1 Reporting Bugs and Requesting Features . . . . . . . . . . . . . . . . . . . . 265
12.2 Contributing Patches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
Contents
xi
12.3 Creating New Plugins . . . . . . . . . . . . . . . . . . .
12.3.1 Where to Keep Plugins in the GATE Hierarchy
12.3.2 What to Call your Plugin . . . . . . . . . . . .
12.3.3 Writing a New PR . . . . . . . . . . . . . . . .
12.3.4 Writing a New VR . . . . . . . . . . . . . . . .
12.3.5 Adding Plugins to the Nightly Build . . . . . .
12.4 Updating this User Guide . . . . . . . . . . . . . . . .
12.4.1 Building the User Guide . . . . . . . . . . . . .
12.4.2 Making Changes to the User Guide . . . . . . .
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CREOLE Plugins
13 Gazetteers
13.1 Introduction to Gazetteers . . . . . . . . . . . . . . .
13.2 ANNIE Gazetteer . . . . . . . . . . . . . . . . . . . .
13.2.1 Creating and Modifying Gazetteer Lists . . .
13.2.2 ANNIE Gazetteer Editor . . . . . . . . . . . .
13.3 Gazetteer Visual Resource - GAZE . . . . . . . . . .
13.3.1 Display Modes . . . . . . . . . . . . . . . . .
13.3.2 Linear Definition Pane . . . . . . . . . . . . .
13.3.3 Linear Definition Toolbar . . . . . . . . . . .
13.3.4 Operations on Linear Definition Nodes . . . .
13.3.5 Gazetteer List Pane . . . . . . . . . . . . . . .
13.3.6 Mapping Definition Pane . . . . . . . . . . . .
13.4 OntoGazetteer . . . . . . . . . . . . . . . . . . . . . .
13.5 Gaze Ontology Gazetteer Editor . . . . . . . . . . . .
13.5.1 The Gaze Gazetteer List and Mapping Editor
13.5.2 The Gaze Ontology Editor . . . . . . . . . . .
13.6 Hash Gazetteer . . . . . . . . . . . . . . . . . . . . .
13.6.1 Prerequisites . . . . . . . . . . . . . . . . . . .
13.6.2 Parameters . . . . . . . . . . . . . . . . . . .
13.7 Flexible Gazetteer . . . . . . . . . . . . . . . . . . . .
13.8 Gazetteer List Collector . . . . . . . . . . . . . . . .
13.9 OntoRoot Gazetteer . . . . . . . . . . . . . . . . . .
13.9.1 How Does it Work? . . . . . . . . . . . . . . .
13.9.2 Initialisation of OntoRoot Gazetteer . . . . .
13.9.3 Simple steps to run OntoRoot Gazetteer . . .
13.10Large KB Gazetteer . . . . . . . . . . . . . . . . . .
13.10.1 Quick usage overview . . . . . . . . . . . . . .
13.10.2 Dictionary setup . . . . . . . . . . . . . . . .
13.10.3 Additional dictionary configuration . . . . . .
13.10.4 Processing Resource Configuration . . . . . .
13.10.5 Runtime configuration . . . . . . . . . . . . .
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Contents
13.10.6 Semantic Enrichment PR . . . . . . . . . . . . . . . . . . . . . . . . . 299
13.11The Shared Gazetteer for multithreaded processing . . . . . . . . . . . . . . 299
14 Working with Ontologies
14.1 Data Model for Ontologies . . . . . . . . . . . . . . . . . . . . . . . . .
14.1.1 Hierarchies of Classes and Restrictions . . . . . . . . . . . . . .
14.1.2 Instances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.1.3 Hierarchies of Properties . . . . . . . . . . . . . . . . . . . . . .
14.1.4 URIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.2 Ontology Event Model . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.2.1 What Happens when a Resource is Deleted? . . . . . . . . . . .
14.3 The Ontology Plugin: Current Implementation . . . . . . . . . . . . . .
14.3.1 The OWLIMOntology Language Resource . . . . . . . . . . . .
14.3.2 The ConnectSesameOntology Language Resource . . . . . . . .
14.3.3 The CreateSesameOntology Language Resource . . . . . . . . .
14.3.4 The OWLIM2 Backwards-Compatible Language Resource . . .
14.3.5 Using Ontology Import Mappings . . . . . . . . . . . . . . . . .
14.3.6 Using BigOWLIM . . . . . . . . . . . . . . . . . . . . . . . . . .
14.4 The Ontology OWLIM2 plugin: backwards-compatible implementation
14.4.1 The OWLIMOntologyLR Language Resource . . . . . . . . . .
14.5 GATE Ontology Editor . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.6 Ontology Annotation Tool . . . . . . . . . . . . . . . . . . . . . . . . .
14.6.1 Viewing Annotated Text . . . . . . . . . . . . . . . . . . . . . .
14.6.2 Editing Existing Annotations . . . . . . . . . . . . . . . . . . .
14.6.3 Adding New Annotations . . . . . . . . . . . . . . . . . . . . .
14.6.4 Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.7 Relation Annotation Tool . . . . . . . . . . . . . . . . . . . . . . . . .
14.7.1 Description of the two views . . . . . . . . . . . . . . . . . . . .
14.7.2 Create new annotation and instance from text selection . . . . .
14.7.3 Create new annotation and add label to existing instance from
selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.7.4 Create and set properties for annotation relation . . . . . . . . .
14.7.5 Delete instance, label or property . . . . . . . . . . . . . . . . .
14.7.6 Differences with OAT and Ontology Editor . . . . . . . . . . . .
14.8 Using the ontology API . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.9 Using the ontology API (old version) . . . . . . . . . . . . . . . . . . .
14.10Ontology-Aware JAPE Transducer . . . . . . . . . . . . . . . . . . . .
14.11Annotating Text with Ontological Information . . . . . . . . . . . . . .
14.12Populating Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.13Ontology API and Implementation Changes . . . . . . . . . . . . . . .
14.13.1 Differences between the implementation plugins . . . . . . . . .
14.13.2 Changes in the Ontology API . . . . . . . . . . . . . . . . . . .
15 Non-English Language Support
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15.1
15.2
15.3
15.4
15.5
French Plugin . . . . . . . . . . . .
German Plugin . . . . . . . . . . .
Romanian Plugin . . . . . . . . . .
Arabic Plugin . . . . . . . . . . . .
Chinese Plugin . . . . . . . . . . .
15.5.1 Chinese Word Segmentation
15.6 Hindi Plugin . . . . . . . . . . . . .
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16 Parsers
16.1 MiniPar Parser . . . . . . . . . . . . . . . . .
16.1.1 Platform Supported . . . . . . . . . . .
16.1.2 Resources . . . . . . . . . . . . . . . .
16.1.3 Parameters . . . . . . . . . . . . . . .
16.1.4 Prerequisites . . . . . . . . . . . . . . .
16.1.5 Grammatical Relationships . . . . . . .
16.2 RASP Parser . . . . . . . . . . . . . . . . . .
16.3 SUPPLE Parser . . . . . . . . . . . . . . . . .
16.3.1 Requirements . . . . . . . . . . . . . .
16.3.2 Building SUPPLE . . . . . . . . . . .
16.3.3 Running the Parser in GATE . . . . .
16.3.4 Viewing the Parse Tree . . . . . . . . .
16.3.5 System Properties . . . . . . . . . . . .
16.3.6 Configuration Files . . . . . . . . . . .
16.3.7 Parser and Grammar . . . . . . . . . .
16.3.8 Mapping Named Entities . . . . . . . .
16.3.9 Upgrading from BuChart to SUPPLE .
16.4 Stanford Parser . . . . . . . . . . . . . . . . .
16.4.1 Input Requirements . . . . . . . . . . .
16.4.2 Initialization Parameters . . . . . . . .
16.4.3 Runtime Parameters . . . . . . . . . .
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17 Machine Learning
17.1 ML Generalities . . . . . . . . . . . . . . . . . . . . . . . . . . .
17.1.1 Some Definitions . . . . . . . . . . . . . . . . . . . . . .
17.1.2 GATE-Specific Interpretation of the Above Definitions .
17.2 Batch Learning PR . . . . . . . . . . . . . . . . . . . . . . . . .
17.2.1 Batch Learning PR Configuration File Settings . . . . .
17.2.2 Case Studies for the Three Learning Types . . . . . . . .
17.2.3 How to Use the Batch Learning PR in GATE Developer
17.2.4 Output of the Batch Learning PR . . . . . . . . . . . . .
17.2.5 Using the Batch Learning PR from the API . . . . . . .
17.3 Machine Learning PR . . . . . . . . . . . . . . . . . . . . . . . .
17.3.1 The DATASET Element . . . . . . . . . . . . . . . . . .
17.3.2 The ENGINE Element . . . . . . . . . . . . . . . . . . .
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Contents
17.3.3
17.3.4
17.3.5
17.3.6
The WEKA Wrapper . . . .
The MAXENT Wrapper . .
The SVM Light Wrapper . .
Example Configuration File
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18 Tools for Alignment Tasks
18.1 Introduction . . . . . . . . . . . . . .
18.2 The Tools . . . . . . . . . . . . . . .
18.2.1 Compound Document . . . .
18.2.2 CompoundDocumentFromXml
18.2.3 Compound Document Editor
18.2.4 Composite Document . . . . .
18.2.5 DeleteMembersPR . . . . . .
18.2.6 SwitchMembersPR . . . . . .
18.2.7 Saving as XML . . . . . . . .
18.2.8 Alignment Editor . . . . . . .
18.2.9 Saving Files and Alignments .
18.2.10 Section-by-Section Processing
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19 Combining GATE and UIMA
19.1 Embedding a UIMA AE in GATE . . . . . . . . . . .
19.1.1 Mapping File Format . . . . . . . . . . . . . .
19.1.2 The UIMA Component Descriptor . . . . . .
19.1.3 Using the AnalysisEnginePR . . . . . . . . .
19.2 Embedding a GATE CorpusController in UIMA . .
19.2.1 Mapping File Format . . . . . . . . . . . . . .
19.2.2 The GATE Application Definition . . . . . . .
19.2.3 Configuring the GATEApplicationAnnotator .
20 More (CREOLE) Plugins
20.1 Verb Group Chunker . . . . . . . . . . . . . . . .
20.2 Noun Phrase Chunker . . . . . . . . . . . . . . .
20.2.1 Differences from the Original . . . . . . .
20.2.2 Using the Chunker . . . . . . . . . . . . .
20.3 TaggerFramework . . . . . . . . . . . . . . . . . .
20.3.1 TreeTagger - Multilingual POS Tagger . .
20.4 Chemistry Tagger . . . . . . . . . . . . . . . . . .
20.4.1 Using the Tagger . . . . . . . . . . . . . .
20.5 Annotating Numbers . . . . . . . . . . . . . . . .
20.5.1 Numbers in Words and Numbers . . . . .
20.5.2 Roman Numerals . . . . . . . . . . . . . .
20.6 Annotating Measurements . . . . . . . . . . . . .
20.7 Annotating and Normalizing Dates . . . . . . . .
20.8 ABNER - A Biomedical Named Entity Recogniser
20.9 Snowball Based Stemmers . . . . . . . . . . . . .
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Contents
20.9.1 Algorithms . . . . . . . . . . . . . . . . . . . .
20.10GATE Morphological Analyzer . . . . . . . . . . . . .
20.10.1 Rule File . . . . . . . . . . . . . . . . . . . . . .
20.11Flexible Exporter . . . . . . . . . . . . . . . . . . . . .
20.12Annotation Set Transfer . . . . . . . . . . . . . . . . .
20.13Schema Enforcer . . . . . . . . . . . . . . . . . . . . .
20.14Information Retrieval in GATE . . . . . . . . . . . . .
20.14.1 Using the IR Functionality in GATE . . . . . .
20.14.2 Using the IR API . . . . . . . . . . . . . . . . .
20.15Websphinx Web Crawler . . . . . . . . . . . . . . . . .
20.15.1 Using the Crawler PR . . . . . . . . . . . . . .
20.16Google Plugin . . . . . . . . . . . . . . . . . . . . . . .
20.17Yahoo Plugin . . . . . . . . . . . . . . . . . . . . . . .
20.17.1 Using the YahooPR . . . . . . . . . . . . . . . .
20.18Google Translator PR . . . . . . . . . . . . . . . . . .
20.19WordNet in GATE . . . . . . . . . . . . . . . . . . . .
20.19.1 The WordNet API . . . . . . . . . . . . . . . .
20.20Kea - Automatic Keyphrase Detection . . . . . . . . .
20.20.1 Using the ‘KEA Keyphrase Extractor’ PR . . .
20.20.2 Using Kea Corpora . . . . . . . . . . . . . . . .
20.21Ontotext JapeC Compiler . . . . . . . . . . . . . . . .
20.22Annotation Merging Plugin . . . . . . . . . . . . . . .
20.23Copying Annotations between Documents . . . . . . .
20.24OpenCalais Plugin . . . . . . . . . . . . . . . . . . . .
20.25LingPipe Plugin . . . . . . . . . . . . . . . . . . . . . .
20.25.1 LingPipe Tokenizer PR . . . . . . . . . . . . . .
20.25.2 LingPipe Sentence Splitter PR . . . . . . . . . .
20.25.3 LingPipe POS Tagger PR . . . . . . . . . . . .
20.25.4 LingPipe NER PR . . . . . . . . . . . . . . . .
20.25.5 LingPipe Language Identifier PR . . . . . . . .
20.26OpenNLP Plugin . . . . . . . . . . . . . . . . . . . . .
20.26.1 Parameters common to all PRs . . . . . . . . .
20.26.2 OpenNLP PRs . . . . . . . . . . . . . . . . . .
20.26.3 Training new models . . . . . . . . . . . . . . .
20.27Tagger MetaMap Plugin . . . . . . . . . . . . . . . . .
20.27.1 Run-time parameters . . . . . . . . . . . . . . .
20.27.2 Upgrading from an earlier version of the plugin
20.28Content Detection Using Boilerpipe . . . . . . . . . . .
20.29Inter Annotator Agreement . . . . . . . . . . . . . . .
20.30Schema Annotation Editor . . . . . . . . . . . . . . . .
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xvi
IV
Contents
The GATE Family: Cloud, MIMIR, Teamware
21 GATE Cloud
21.1 GATE Cloud services: an overview . . . . . . . . . . .
21.2 Comparison with other systems . . . . . . . . . . . . .
21.3 How to buy services . . . . . . . . . . . . . . . . . . . .
21.4 Pricing and discounts . . . . . . . . . . . . . . . . . . .
21.5 Annotation Jobs on GATECloud.net . . . . . . . . . .
21.5.1 The Annotation Service Charges Explained . . .
21.5.2 Annotation Job Execution in Detail . . . . . . .
21.6 Running Custom Annotation Jobs on GATECloud.net
21.6.1 Preparing Your Application: The Basics . . . .
21.6.2 The GATECloud.net environment . . . . . . . .
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22 GATE Teamware: A Web-based Collaborative Corpus Annotation Tool
22.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22.2 Requirements for Multi-Role Collaborative Annotation Environments . . . .
22.2.1 Typical Division of Labour . . . . . . . . . . . . . . . . . . . . . . . .
22.2.2 Remote, Scalable Data Storage . . . . . . . . . . . . . . . . . . . . .
22.2.3 Automatic annotation services . . . . . . . . . . . . . . . . . . . . . .
22.2.4 Workflow Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22.3 Teamware: Architecture, Implementation, and Examples . . . . . . . . . . .
22.3.1 Data Storage Service . . . . . . . . . . . . . . . . . . . . . . . . . . .
22.3.2 Annotation Services . . . . . . . . . . . . . . . . . . . . . . . . . . .
22.3.3 The Executive Layer . . . . . . . . . . . . . . . . . . . . . . . . . . .
22.3.4 The User Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22.4 Practical Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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23 GATE Mı́mir
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Appendices
A Change Log
A.1 Version 6.1 (April 2011) . . . . . . . . . . . .
A.1.1 New CREOLE Plugins . . . . . . . . .
A.1.2 Other new features and improvements
A.2 Version 6.0 (November 2010) . . . . . . . . . .
A.2.1 Major new features . . . . . . . . . . .
A.2.2 Breaking changes . . . . . . . . . . . .
A.2.3 Other new features and bugfixes . . . .
A.3 Version 5.2.1 (May 2010) . . . . . . . . . . . .
A.4 Version 5.2 (April 2010) . . . . . . . . . . . .
A.4.1 JAPE and JAPE-related . . . . . . . .
A.4.2 Other Changes . . . . . . . . . . . . .
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Contents
xvii
A.5 Version 5.1 (December 2009) . . . . . . . . . .
A.5.1 New Features . . . . . . . . . . . . . .
A.5.2 JAPE improvements . . . . . . . . . .
A.5.3 Other improvements and bug fixes . .
A.6 Version 5.0 (May 2009) . . . . . . . . . . . . .
A.6.1 Major New Features . . . . . . . . . .
A.6.2 Other New Features and Improvements
A.6.3 Specific Bug Fixes . . . . . . . . . . .
A.7 Version 4.0 (July 2007) . . . . . . . . . . . . .
A.7.1 Major New Features . . . . . . . . . .
A.7.2 Other New Features and Improvements
A.7.3 Bug Fixes and Optimizations . . . . .
A.8 Version 3.1 (April 2006) . . . . . . . . . . . .
A.8.1 Major New Features . . . . . . . . . .
A.8.2 Other New Features and Improvements
A.8.3 Bug Fixes . . . . . . . . . . . . . . . .
A.9 January 2005 . . . . . . . . . . . . . . . . . .
A.10 December 2004 . . . . . . . . . . . . . . . . .
A.11 September 2004 . . . . . . . . . . . . . . . . .
A.12 Version 3 Beta 1 (August 2004) . . . . . . . .
A.13 July 2004 . . . . . . . . . . . . . . . . . . . .
A.14 June 2004 . . . . . . . . . . . . . . . . . . . .
A.15 April 2004 . . . . . . . . . . . . . . . . . . . .
A.16 March 2004 . . . . . . . . . . . . . . . . . . .
A.17 Version 2.2 – August 2003 . . . . . . . . . . .
A.18 Version 2.1 – February 2003 . . . . . . . . . .
A.19 June 2002 . . . . . . . . . . . . . . . . . . . .
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B Version 5.1 Plugins Name Map
C Design Notes
C.1 Patterns . . . . . . . . . . . .
C.1.1 Components . . . . . .
C.1.2 Model, view, controller
C.1.3 Interfaces . . . . . . .
C.2 Exception Handling . . . . . .
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D JAPE: Implementation
D.1 Formal Description of the JAPE Grammar
D.2 Relation to CPSL . . . . . . . . . . . . . .
D.3 Initialisation of a JAPE Grammar . . . . .
D.4 Execution of JAPE Grammars . . . . . . .
D.5 Using a Different Java Compiler . . . . . .
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571
E Ant Tasks for GATE
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573
Contents
1
E.1 Declaring the Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573
E.2 The packagegapp task - bundling an application with its dependencies . . . 573
E.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573
E.2.2 Basic Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 574
E.2.3 Handling Non-Plugin Resources . . . . . . . . . . . . . . . . . . . . . 575
E.2.4 Streamlining your Plugins . . . . . . . . . . . . . . . . . . . . . . . . 578
E.2.5 Bundling Extra Resources . . . . . . . . . . . . . . . . . . . . . . . . 578
E.3 The expandcreoles Task - Merging Annotation-Driven Config into creole.xml 579
F Named-Entity State Machine Patterns
F.1 Main.jape . . . . . . . . . . . . . . . .
F.2 first.jape . . . . . . . . . . . . . . . . .
F.3 firstname.jape . . . . . . . . . . . . . .
F.4 name.jape . . . . . . . . . . . . . . . .
F.4.1 Person . . . . . . . . . . . . . .
F.4.2 Location . . . . . . . . . . . . .
F.4.3 Organization . . . . . . . . . .
F.4.4 Ambiguities . . . . . . . . . . .
F.4.5 Contextual information . . . . .
F.5 name post.jape . . . . . . . . . . . . .
F.6 date pre.jape . . . . . . . . . . . . . .
F.7 date.jape . . . . . . . . . . . . . . . . .
F.8 reldate.jape . . . . . . . . . . . . . . .
F.9 number.jape . . . . . . . . . . . . . . .
F.10 address.jape . . . . . . . . . . . . . . .
F.11 url.jape . . . . . . . . . . . . . . . . .
F.12 identifier.jape . . . . . . . . . . . . . .
F.13 jobtitle.jape . . . . . . . . . . . . . . .
F.14 final.jape . . . . . . . . . . . . . . . . .
F.15 unknown.jape . . . . . . . . . . . . . .
F.16 name context.jape . . . . . . . . . . .
F.17 org context.jape . . . . . . . . . . . . .
F.18 loc context.jape . . . . . . . . . . . . .
F.19 clean.jape . . . . . . . . . . . . . . . .
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G Part-of-Speech Tags used in the Hepple Tagger
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References
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2
Contents
Part I
GATE Basics
3
Chapter 1
Introduction
Software documentation is like sex: when it is good, it is very, very good; and
when it is bad, it is better than nothing. (Anonymous.)
There are two ways of constructing a software design: one way is to make it so
simple that there are obviously no deficiencies; the other way is to make it so
complicated that there are no obvious deficiencies. (C.A.R. Hoare)
A computer language is not just a way of getting a computer to perform operations but rather that it is a novel formal medium for expressing ideas about
methodology. Thus, programs must be written for people to read, and only incidentally for machines to execute. (The Structure and Interpretation of Computer
Programs, H. Abelson, G. Sussman and J. Sussman, 1985.)
If you try to make something beautiful, it is often ugly. If you try to make
something useful, it is often beautiful. (Oscar Wilde)1
GATE2 is an infrastructure for developing and deploying software components that process
human language. It is nearly 15 years old and is in active use for all types of computational
task involving human language. GATE excels at text analysis of all shapes and sizes. From
large corporations to small startups, from €multi-million research consortia to undergraduate
projects, our user community is the largest and most diverse of any system of this type, and
is spread across all but one of the continents3 .
GATE is open source free software; users can obtain free support from the user and developer
community via GATE.ac.uk or on a commercial basis from our industrial partners. We
are the biggest open source language processing project with a development team more
than double the size of the largest comparable projects (many of which are integrated with
1
These were, at least, our ideals; of course we didn’t completely live up to them. . .
If you’ve read the overview at http://gate.ac.uk/overview.html, you may prefer to skip to Section 1.1.
3
Rumours that we’re planning to send several of the development team to Antarctica on one-way tickets
are false, libellous and wishful thinking.
2
5
6
Introduction
GATE4 ). More than €5 million has been invested in GATE development5 ; our objective is
to make sure that this continues to be money well spent for all GATE’s users.
The GATE family of tools has grown over the years to include a desktop client for developers,
a workflow-based web application, a Java library, an architecture and a process. GATE is:
• an IDE, GATE Developer: an integrated development environment6 for language
processing components bundled with a very widely used Information Extraction system
and a comprehensive set of other plugins
• a cloud computing solution for hosted large-scale text processing, GATE Cloud
(http://gatecloud.net/). See also Chapter 21.
• a web app, GATE Teamware: a collaborative annotation environment for factorystyle semantic annotation projects built around a workflow engine and a heavilyoptimised backend service infrastructure. See also Chapter 22.
• a multi-paradigm search repository, GATE Mı́mir, which can be used to index and
search over text, annotations, semantic schemas (ontologies), and semantic meta-data
(instance data). It allows queries that arbitrarily mix full-text, structural, linguistic
and semantic queries and that can scale to terabytes of text. See also Chapter 23.
• a framework, GATE Embedded: an object library optimised for inclusion in diverse
applications giving access to all the services used by GATE Developer and more.
• an architecture: a high-level organisational picture of how language processing software
composition.
• a process for the creation of robust and maintainable services.
We also develop:
• a wiki/CMS, GATE Wiki (http://gatewiki.sf.net/), mainly to host our own websites
and as a testbed for some of our experiments
For more information on the GATE family see http://gate.ac.uk/family/ and also Part IV
of this book.
One of our original motivations was to remove the necessity for solving common engineering
problems before doing useful research, or re-engineering before deploying research results
into applications. Core functions of GATE take care of the lion’s share of the engineering:
4
Our philosophy is reuse not reinvention, so we integrate and interoperate with other systems e.g.:
LingPipe, OpenNLP, UIMA, and many more specific tools.
5
This is the figure for direct Sheffield-based investment only and therefore an underestimate.
6
GATE Developer and GATE Embedded are bundled, and in older distributions were referred to just as
‘GATE’.
Introduction
7
• modelling and persistence of specialised data structures
• measurement, evaluation, benchmarking (never believe a computing researcher who
hasn’t measured their results in a repeatable and open setting!)
• visualisation and editing of annotations, ontologies, parse trees, etc.
• a finite state transduction language for rapid prototyping and efficient implementation
of shallow analysis methods (JAPE)
• extraction of training instances for machine learning
• pluggable machine learning implementations (Weka, SVM Light, ...)
On top of the core functions GATE includes components for diverse language processing
tasks, e.g. parsers, morphology, tagging, Information Retrieval tools, Information Extraction
components for various languages, and many others. GATE Developer and Embedded are
supplied with an Information Extraction system (ANNIE) which has been adapted and
evaluated very widely (numerous industrial systems, research systems evaluated in MUC,
TREC, ACE, DUC, Pascal, NTCIR, etc.). ANNIE is often used to create RDF or OWL
(metadata) for unstructured content (semantic annotation).
GATE version 1 was written in the mid-1990s; at the turn of the new millennium we completely rewrote the system in Java; version 5 was released in June 2009; and version 6 — in
November 2010. We believe that GATE is the leading system of its type, but as scientists
we have to advise you not to take our word for it; that’s why we’ve measured our software
in many of the competitive evaluations over the last decade-and-a-half (MUC, TREC, ACE,
DUC and more; see Section 1.4 for details). We invite you to give it a try, to get involved
with the GATE community, and to contribute to human language science, engineering and
development.
This book describes how to use GATE to develop language processing components, test their
performance and deploy them as parts of other applications. In the rest of this chapter:
• Section 1.1 describes the best way to use this book;
• Section 1.2 briefly notes that the context of GATE is applied language processing, or
Language Engineering;
• Section 1.3 gives an overview of developing using GATE;
• Section 1.4 lists publications describing GATE performance in evaluations;
• Section 1.5 outlines what is new in the current version of GATE;
• Section 1.6 lists other publications about GATE.
8
Introduction
Note: if you don’t see the component you need in this document, or if we mention a component that you can’t see in the software, contact [email protected] –
various components are developed by our collaborators, who we will be happy to put you
in contact with. (Often the process of getting a new component is as simple as typing the
URL into GATE Developer; the system will do the rest.)
1.1
How to Use this Text
The material presented in this book ranges from the conceptual (e.g. ‘what is software
architecture?’) to practical instructions for programmers (e.g. how to deal with GATE
exceptions) and linguists (e.g. how to write a pattern grammar). Furthermore, GATE’s
highly extensible nature means that new functionality is constantly being added in the form
of new plugins. Important functionality is as likely to be located in a plugin as it is to
be integrated into the GATE core. This presents something of an organisational challenge.
Our (no doubt imperfect) solution is to divide this book into three parts. Part I covers
installation, using the GATE Developer GUI and using ANNIE, as well as providing some
background and theory. We recommend the new user to begin with Part I. Part II covers
the more advanced of the core GATE functionality; the GATE Embedded API and JAPE
pattern language among other things. Part III provides a reference for the numerous plugins
that have been created for GATE. Although ANNIE provides a good starting point, the
user will soon wish to explore other resources, and so will need to consult this part of the
text. We recommend that Part III be used as a reference, to be dipped into as necessary. In
Part III, plugins are grouped into broad areas of functionality.
1.2
Context
GATE can be thought of as a Software Architecture for Language Engineering
[Cunningham 00].
‘Software Architecture’ is used rather loosely here to mean computer infrastructure for software development, including development environments and frameworks, as well as the more
usual use of the term to denote a macro-level organisational structure for software systems
[Shaw & Garlan 96].
Language Engineering (LE) may be defined as:
. . . the discipline or act of engineering software systems that perform tasks involving processing human language. Both the construction process and its outputs
7
Follow the ‘support’ link from http://gate.ac.uk/ to subscribe to the mailing list.
Introduction
9
are measurable and predictable. The literature of the field relates to both application of relevant scientific results and a body of practice. [Cunningham 99a]
The relevant scientific results in this case are the outputs of Computational Linguistics, Natural Language Processing and Artificial Intelligence in general. Unlike these other disciplines,
LE, as an engineering discipline, entails predictability, both of the process of constructing LEbased software and of the performance of that software after its completion and deployment
in applications.
Some working definitions:
1. Computational Linguistics (CL): science of language that uses computation as an
investigative tool.
2. Natural Language Processing (NLP): science of computation whose subject matter is data structures and algorithms for computer processing of human language.
3. Language Engineering (LE): building NLP systems whose cost and outputs are
measurable and predictable.
4. Software Architecture: macro-level organisational principles for families of systems.
In this context is also used as infrastructure.
5. Software Architecture for Language Engineering (SALE): software infrastructure, architecture and development tools for applied CL, NLP and LE.
(Of course the practice of these fields is broader and more complex than these definitions.)
In the scientific endeavours of NLP and CL, GATE’s role is to support experimentation. In
this context GATE’s significant features include support for automated measurement (see
Chapter 10), providing a ‘level playing field’ where results can easily be repeated across
different sites and environments, and reducing research overheads in various ways.
1.3
1.3.1
Overview
Developing and Deploying Language Processing Facilities
GATE as an architecture suggests that the elements of software systems that process natural
language can usefully be broken down into various types of component, known as resources8 .
8
The terms ‘resource’ and ‘component’ are synonymous in this context. ‘Resource’ is used instead of just
‘component’ because it is a common term in the literature of the field: cf. the Language Resources and
Evaluation conference series [LREC-1 98, LREC-2 00].
10
Introduction
Components are reusable software chunks with well-defined interfaces, and are a popular
architectural form, used in Sun’s Java Beans and Microsoft’s .Net, for example. GATE
components are specialised types of Java Bean, and come in three flavours:
• LanguageResources (LRs) represent entities such as lexicons, corpora or ontologies;
• ProcessingResources (PRs) represent entities that are primarily algorithmic, such as
parsers, generators or ngram modellers;
• VisualResources (VRs) represent visualisation and editing components that participate
in GUIs.
These definitions can be blurred in practice as necessary.
Collectively, the set of resources integrated with GATE is known as CREOLE: a Collection
of REusable Objects for Language Engineering. All the resources are packaged as Java
Archive (or ‘JAR’) files, plus some XML configuration data. The JAR and XML files are
made available to GATE by putting them on a web server, or simply placing them in the
local file space. Section 1.3.2 introduces GATE’s built-in resource set.
When using GATE to develop language processing functionality for an application, the
developer uses GATE Developer and GATE Embedded to construct resources of the three
types. This may involve programming, or the development of Language Resources such as
grammars that are used by existing Processing Resources, or a mixture of both. GATE
Developer is used for visualisation of the data structures produced and consumed during
processing, and for debugging, performance measurement and so on. For example, figure 1.1
is a screenshot of one of the visualisation tools.
GATE Developer is analogous to systems like Mathematica for Mathematicians, or JBuilder
for Java programmers: it provides a convenient graphical environment for research and
development of language processing software.
When an appropriate set of resources have been developed, they can then be embedded in
the target client application using GATE Embedded. GATE Embedded is supplied as a
series of JAR files.9 To embed GATE-based language processing facilities in an application,
these JAR files are all that is needed, along with JAR files and XML configuration files for
the various resources that make up the new facilities.
9
The main JAR file (gate.jar) supplies the framework. Built-in resources and various 3rd-party libraries
are supplied as separate JARs; for example (guk.jar, the GATE Unicode Kit.) contains Unicode support
(e.g. additional input methods for languages not currently supported by the JDK). They are separate because
the latter has to be a Java extension with a privileged security profile.
Introduction
11
Figure 1.1: One of GATE’s visual resources
1.3.2
Built-In Components
GATE includes resources for common LE data structures and algorithms, including documents, corpora and various annotation types, a set of language analysis components for
Information Extraction and a range of data visualisation and editing components.
GATE supports documents in a variety of formats including XML, RTF, email, HTML,
SGML and plain text. In all cases the format is analysed and converted into a single unified model of annotation. The annotation format is a modified form of the TIPSTER format [Grishman 97] which has been made largely compatible with the Atlas format
[Bird & Liberman 99], and uses the now standard mechanism of ‘stand-off markup’. GATE
documents, corpora and annotations are stored in databases of various sorts, visualised via
the development environment, and accessed at code level via the framework. See Chapter 5
for more details of corpora etc.
A family of Processing Resources for language analysis is included in the shape of ANNIE,
A Nearly-New Information Extraction system. These components use finite state techniques
to implement various tasks from tokenisation to semantic tagging or verb phrase chunking.
All ANNIE components communicate exclusively via GATE’s document and annotation
resources. See Chapter 6 for more details. Other CREOLE resources are described in
Part III.
12
Introduction
1.3.3
Additional Facilities in GATE Developer/Embedded
Three other facilities in GATE deserve special mention:
• JAPE, a Java Annotation Patterns Engine, provides regular-expression based pattern/action rules over annotations – see Chapter 8.
• The ‘annotation diff’ tool in the development environment implements performance
metrics such as precision and recall for comparing annotations. Typically a language
analysis component developer will mark up some documents by hand and then use these
along with the diff tool to automatically measure the performance of the components.
See Chapter 10.
• GUK, the GATE Unicode Kit, fills in some of the gaps in the JDK’s10 support for
Unicode, e.g. by adding input methods for various languages from Urdu to Chinese.
See Section 3.10.2 for more details.
1.3.4
An Example
This section gives a very brief example of a typical use of GATE to develop and deploy
language processing capabilities in an application, and to generate quantitative results for
scientific publication.
Let’s imagine that a developer called Fatima is building an email client11 for Cyberdyne
Systems’ large corporate Intranet. In this application she would like to have a language
processing system that automatically spots the names of people in the corporation and
transforms them into mailto hyperlinks.
A little investigation shows that GATE’s existing components can be tailored to this purpose.
Fatima starts up GATE Developer, and creates a new document containing some example
emails. She then loads some processing resources that will do named-entity recognition (a
tokeniser, gazetteer and semantic tagger), and creates an application to run these components
on the document in sequence. Having processed the emails, she can see the results in one of
several viewers for annotations.
The GATE components are a decent start, but they need to be altered to deal specially
with people from Cyberdyne’s personnel database. Therefore Fatima creates new ‘cyber-’
versions of the gazetteer and semantic tagger resources, using the ‘bootstrap’ tool. This tool
creates a directory structure on disk that has some Java stub code, a Makefile and an XML
10
JDK: Java Development Kit, Sun Microsystem’s Java implementation. Unicode support is being actively
improved by Sun, but at the time of writing many languages are still unsupported. In fact, Unicode itself
doesn’t support all languages, e.g. Sylheti; hopefully this will change in time.
11
Perhaps because Outlook Express trashed her mail folder again, or because she got tired of Microsoftspecific viruses and hadn’t heard of Gmail or Thunderbird.
Introduction
13
configuration file. After several hours struggling with badly written documentation, Fatima
manages to compile the stubs and create a JAR file containing the new resources. She tells
GATE Developer the URL of these files12 , and the system then allows her to load them in
the same way that she loaded the built-in resources earlier on.
Fatima then creates a second copy of the email document, and uses the annotation editing
facilities to mark up the results that she would like to see her system producing. She saves
this and the version that she ran GATE on into her serial datastore. From now on she can
follow this routine:
1. Run her application on the email test corpus.
2. Check the performance of the system by running the ‘annotation diff’ tool to compare
her manual results with the system’s results. This gives her both percentage accuracy
figures and a graphical display of the differences between the machine and human
outputs.
3. Make edits to the code, pattern grammars or gazetteer lists in her resources, and
recompile where necessary.
4. Tell GATE Developer to re-initialise the resources.
5. Go to 1.
To make the alterations that she requires, Fatima re-implements the ANNIE gazetteer so that
it regenerates itself from the local personnel data. She then alters the pattern grammar in the
semantic tagger to prioritise recognition of names from that source. This latter job involves
learning the JAPE language (see Chapter 8), but as this is based on regular expressions it
isn’t too difficult.
Eventually the system is running nicely, and her accuracy is 93% (there are still some problem cases, e.g. when people use nicknames, but the performance is good enough for production use). Now Fatima stops using GATE Developer and works instead on embedding
the new components in her email application using GATE Embedded. This application is
written in Java, so embedding is very easy13 : the GATE JAR files are added to the project
CLASSPATH, the new components are placed on a web server, and with a little code to do
initialisation, loading of components and so on, the job is finished in half a day – the code
to talk to GATE takes up only around 150 lines of the eventual application, most of which
is just copied from the example in the sheffield.examples.StandAloneAnnie class.
Because Fatima is worried about Cyberdyne’s unethical policy of developing Skynet to help
the large corporates of the West strengthen their strangle-hold over the World, she wants
to get a job as an academic instead (so that her conscience will only have to cope with the
12
While developing, she uses a file:/... URL; for deployment she can put them on a web server.
Languages other than Java require an additional interface layer, such as JNI, the Java Native Interface,
which is in C.
13
14
Introduction
torture of students, as opposed to humanity). She takes the accuracy measures that she
has attained for her system and writes a paper for the Journal of Nasturtium Logarithm
Incitement describing the approach used and the results obtained. Because she used GATE
for development, she can cite the repeatability of her experiments and offer access to example
binary versions of her software by putting them on an external web server.
And everybody lived happily ever after.
1.4
Some Evaluations
This section contains an incomplete list of publications describing systems that used GATE in
competitive quantitative evaluation programmes. These programmes have had a significant
impact on the language processing field and the widespread presence of GATE is some
measure of the maturity of the system and of our understanding of its likely performance on
diverse text processing tasks.
[Li et al. 07d] describes the performance of an SVM-based learning system in the NTCIR-6
Patent Retrieval Task. The system achieved the best result on two of three measures
used in the task evaluation, namely the R-Precision and F-measure. The system obtained close to the best result on the remaining measure (A-Precision).
[Saggion 07] describes a cross-source coreference resolution system based on semantic clustering. It uses GATE for information extraction and the SUMMA system to create summaries and semantic representations of documents. One system configuration
ranked 4th in the Web People Search 2007 evaluation.
[Saggion 06] describes a cross-lingual summarization system which uses SUMMA components and the Arabic plugin available in GATE to produce summaries in English from
a mixture of English and Arabic documents.
Open-Domain Question Answering: The University of Sheffield has a long history
of research into open-domain question answering. GATE has formed the basis of much of this research resulting in systems which have ranked highly during independent evaluations since 1999. The first successful question answering
system developed at the University of Sheffield was evaluated as part of TREC
8 and used the LaSIE information extraction system (the forerunner of ANNIE)
which was distributed with GATE [Humphreys et al. 99]. Further research was
reported in [Scott & Gaizauskas. 00], [Greenwood et al. 02], [Gaizauskas et al. 03],
[Gaizauskas et al. 04] and [Gaizauskas et al. 05]. In 2004 the system was ranked 9th
out of 28 participating groups.
[Saggion 04] describes techniques for answering definition questions. The system uses definition patterns manually implemented in GATE as well as learned JAPE patterns
Introduction
15
induced from a corpus. In 2004, the system was ranked 4th in the TREC/QA evaluations.
[Saggion & Gaizauskas 04b] describes a multidocument summarization system implemented using summarization components compatible with GATE (the SUMMA system). The system was ranked 2nd in the Document Understanding Evaluation programmes.
[Maynard et al. 03e] and [Maynard et al. 03d] describe participation in the TIDES
surprise language program. ANNIE was adapted to Cebuano with four person days of
effort, and achieved an F-measure of 77.5%. Unfortunately, ours was the only system
participating!
[Maynard et al. 02b] and [Maynard et al. 03b] describe results obtained on systems
designed for the ACE task (Automatic Content Extraction). Although a comparison to other participating systems cannot be revealed due to the stipulations of ACE,
results show 82%-86% precision and recall.
[Humphreys et al. 98] describes the LaSIE-II system used in MUC-7.
[Gaizauskas et al. 95] describes the LaSIE-II system used in MUC-6.
1.5
Recent Changes
This section details recent changes made to GATE. Appendix A provides a complete change
log.
1.5.1
Version 6.1 (April 2011)
New CREOLE Plugins
Tagger Numbers to annotate many kinds of numbers in documents and determine their
numeric values. The tagger can annotate numbers expressed in many forms including Arabic and Roman numerals, words (in English, French, German and Spanish) and scientific
notation (4.3e6 = 4300000). See section 20.5 for full details.
Tagger Measurements to annotate many different forms of measurement expressions (“5.5
metres”, “1 minute 30 seconds”, “10 to 15 pounds”, etc.) along with their normalized values
in SI units. See section 20.6 for full details.
Tagger Boilerpipe, which contains a boilerpipe14 based PR for performing content detection. See section 20.28 for full details.
14
http://code.google.com/p/boilerpipe/
16
Introduction
Tagger DateNormalizer to annotate and normalize dates within a document. See section 20.7 for full details.
Schema Tools providing a “Schema Enforcer” PR that can be used to create a clean output
annotation set based on a set of annotation schemas. See section 20.13 for full details.
Teamware Tools providing a new PR called QA Summariser for Teamware. When documents are annotated using GATE Teamware, this PR can be used for generating a summary
of agreements among annotators. See section 10.7 for full details.
Tagger MetaMap has been rewritten to make use of the new MetaMap Java API features.
There are numerous performance enhancements and bug fixes detailed in section 20.27. Note
that this version of the plugin is not compatible with the version provided in GATE 6.0,
though this earlier version is still available in the Obsolete directory if required.
Other new features and improvements
Added support for handling controller events to JAPE by making it possible to define
ControllerStarted, ControllerFinished, and ControllerAborted code blocks in a JAPE
file (see section 8.6.5).
JAPE Java right-hand-side code can now access an ActionContext object through the predefined field ctx which allows access to the corpus LR and the transducer PR and their
features (see section 8.6.5).
Three new optional attributes can be specified in <GATECONFIG> element of gate.xml or
local configuration file:
• addNamespaceFeatures - set to ”true” to deserialize namespace prefix and URI
information as features.
• namespaceURI - The feature name to use that will hold the namespace URI of the
element, e.g. ”namespace”
• namespacePrefix - The feature name to use that will hold the namespace prefix of
the element, e.g. ”prefix”
Setting these attributes will alter GATE’s default namespace deserialization behaviour to
remove the namespace prefix and add it as a feature, along with the namespace URI. This
allows namespace-prefixed elements in the Original markups annotation set to be matched
with JAPE expressions, and also allows namespace scope to be added to new annotations
when serialized to XML. See 5.5.2 for details.
Searchable Serial Datastores (Lucene-based) are now portable and can be moved across
different systems. Also, several GUI improvements have been made to ease the creation of
Lucene datastores. See chapter 9 for details.
Introduction
17
The populate method that allowed populating corpus from a trecweb file has been made
more generic to accept a tag. The method extracts content between the start and end of
this tag to create new documents. In GATE Developer, right-clicking on an instance of the
Corpus and choosing the option “Populate from Single Concatenated File” allows users to
populate the corpus using this functionality. See Section 7.4.5 for more details.
Fixed a regression in the JAPE parser that prevented the use of RHS macros that refer to
a LHS label (named blocks :label { ... } and assignments :label.Type = {}
Enhanced the Groovy scriptable controller with some features inspired by the realtime controller, in particular the ability to ignore exceptions thrown by PRs and the ability to limit
the running time of certain PRs. See section 7.16.3 for details.
The Ontology and Gazetteer LKB plugins have been upgraded to use Sesame 3.2.3 and
OWLIM 3.5.
The Websphinx Crawler PR (section 20.15) has new runtime parameters for controlling the
maximum page size and spoofing the user-agent.
A few bug fixes and improvements to the “recover” logic of the packagegapp Ant task (see
section E.2).
. . . and many other smaller bugfixes.
Note: As of version 6.1, GATE Developer and Embedded require Java 6 or later
and will no longer run on Java 5. If you require Java 5 compatibility you should use
GATE 6.0.
1.6
Further Reading
Lots of documentation lives on the GATE web site, including:
• GATE online tutorials;
• the main system documentation tree;
• JavaDoc API documentation;
• HTML of the source code;
• comprehensive list of GATE plugins.
For more details about Sheffield University’s work in human language processing see the NLP
group pages or A Definition and Short History of Language Engineering ([Cunningham 99a]).
For more details about Information Extraction see IE, a User Guide or the GATE IE pages.
18
Introduction
A list of publications on GATE and projects that use it (some of which are available on-line
from http://gate.ac.uk/gate/doc/papers.html):
2010
[Bontcheva et al. 10] describes the Teamware web-based collaborative annotation environment, emphasising the different roles that users play in the corpus annotation process.
[Damljanovic 10] presents the use of GATE in the development of controlled natural language interfaces. There is other related work by Damljanovic, Agatonovic, and Cunningham on using GATE to build natural language interfaces for quering ontologies.
[Aswani & Gaizauskas 10] discusses the use of GATE to process South Asian languages
(Hindi and Gujarati).
2009
[Saggion & Funk 09] focuses in detail on the use of GATE for mining opinions and facts
for business intelligence gathering from web content.
[Aswani & Gaizauskas 09] presents in more detail the text alignment component of
GATE.
[Bontcheva et al. 09] is the ‘Human Language Technologies’ chapter of ‘Semantic Knowledge Management’ (John Davies, Marko Grobelnik and Dunja Mladeni eds.)
[Damljanovic et al. 09] discusses the use of semantic annotation for software engineering,
as part of the TAO research project.
[Laclavik & Maynard 09] reviews the current state of the art in email processing and
communication research, focusing on the roles played by email in information management, and commercial and research efforts to integrate a semantic-based approach to
email.
[Li et al. 09] investigates two techniques for making SVMs more suitable for language learning tasks. Firstly, an SVM with uneven margins (SVMUM) is proposed to deal with
the problem of imbalanced training data. Secondly, SVM active learning is employed
in order to alleviate the difficulty in obtaining labelled training data. The algorithms
are presented and evaluated on several Information Extraction (IE) tasks.
2008
[Agatonovic et al. 08] presents our approach to automatic patent enrichment, tested in
large-scale, parallel experiments on USPTO and EPO documents.
Introduction
19
[Damljanovic et al. 08] presents Question-based Interface to Ontologies (QuestIO) - a tool
for querying ontologies using unconstrained language-based queries.
[Damljanovic & Bontcheva 08] presents a semantic-based prototype that is made for
an open-source software engineering project with the goal of exploring methods for
assisting open-source developers and software users to learn and maintain the system
without major effort.
[Della Valle et al. 08] presents ServiceFinder.
[Li & Cunningham 08] describes our SVM-based system and several techniques we developed successfully to adapt SVM for the specific features of the F-term patent classification task.
[Li & Bontcheva 08] reviews the recent developments in applying geometric and quantum
mechanics methods for information retrieval and natural language processing.
[Maynard 08] investigates the state of the art in automatic textual annotation tools, and
examines the extent to which they are ready for use in the real world.
[Maynard et al. 08a] discusses methods of measuring the performance of ontology-based
information extraction systems, focusing particularly on the Balanced Distance Metric
(BDM), a new metric we have proposed which aims to take into account the more
flexible nature of ontologically-based applications.
[Maynard et al. 08b] investigates NLP techniques for ontology population, using a combination of rule-based approaches and machine learning.
[Tablan et al. 08] presents the QuestIO system a natural language interface for accessing
structured information, that is domain independent and easy to use without training.
2007
[Funk et al. 07a] describes an ontologically based approach to multi-source, multilingual
information extraction.
[Funk et al. 07b] presents a controlled language for ontology editing and a software implementation, based partly on standard NLP tools, for processing that language and
manipulating an ontology.
[Maynard et al. 07a] proposes a methodology to capture (1) the evolution of metadata
induced by changes to the ontologies, and (2) the evolution of the ontology induced by
changes to the underlying metadata.
[Maynard et al. 07b] describes the development of a system for content mining using domain ontologies, which enables the extraction of relevant information to be fed into
models for analysis of financial and operational risk and other business intelligence
applications such as company intelligence, by means of the XBRL standard.
20
Introduction
[Saggion 07] describes experiments for the cross-document coreference task in SemEval
2007. Our cross-document coreference system uses an in-house agglomerative clustering
implementation to group documents referring to the same entity.
[Saggion et al. 07] describes the application of ontology-based extraction and merging in
the context of a practical e-business application for the EU MUSING Project where the
goal is to gather international company intelligence and country/region information.
[Li et al. 07a] introduces a hierarchical learning approach for IE, which uses the target
ontology as an essential part of the extraction process, by taking into account the
relations between concepts.
[Li et al. 07b] proposes some new evaluation measures based on relations among classification labels, which can be seen as the label relation sensitive version of important
measures such as averaged precision and F-measure, and presents the results of applying the new evaluation measures to all submitted runs for the NTCIR-6 F-term patent
classification task.
[Li et al. 07c] describes the algorithms and linguistic features used in our participating
system for the opinion analysis pilot task at NTCIR-6.
[Li et al. 07d] describes our SVM-based system and the techniques we used to adapt the
approach for the specifics of the F-term patent classification subtask at NTCIR-6
Patent Retrieval Task.
[Li & Shawe-Taylor 07] studies Japanese-English cross-language patent retrieval using
Kernel Canonical Correlation Analysis (KCCA), a method of correlating linear relationships between two variables in kernel defined feature spaces.
2006
[Aswani et al. 06] (Proceedings of the 5th International Semantic Web Conference
(ISWC2006)) In this paper the problem of disambiguating author instances in ontology is addressed. We describe a web-based approach that uses various features such
as publication titles, abstract, initials and co-authorship information.
[Bontcheva et al. 06a] ‘Semantic Annotation and Human Language Technology’, contribution to ‘Semantic Web Technology: Trends and Research’ (Davies, Studer and Warren, eds.)
[Bontcheva et al. 06b] ‘Semantic Information Access’, contribution to ‘Semantic Web
Technology: Trends and Research’ (Davies, Studer and Warren, eds.)
[Bontcheva & Sabou 06] presents an ontology learning approach that 1) exploits a range
of information sources associated with software projects and 2) relies on techniques
that are portable across application domains.
Introduction
21
[Davis et al. 06] describes work in progress concerning the application of Controlled Language Information Extraction - CLIE to a Personal Semantic Wiki - Semper- Wiki,
the goal being to permit users who have no specialist knowledge in ontology tools or
languages to semi-automatically annotate their respective personal Wiki pages.
[Li & Shawe-Taylor 06] studies a machine learning algorithm based on KCCA for crosslanguage information retrieval. The algorithm is applied to Japanese-English crosslanguage information retrieval.
[Maynard et al. 06] discusses existing evaluation metrics, and proposes a new method for
evaluating the ontology population task, which is general enough to be used in a variety
of situation, yet more precise than many current metrics.
[Tablan et al. 06b] describes an approach that allows users to create and edit ontologies
simply by using a restricted version of the English language. The controlled language
described is based on an open vocabulary and a restricted set of grammatical constructs.
[Tablan et al. 06a] describes the creation of linguistic analysis and corpus search tools for
Sumerian, as part of the development of the ETCSL.
[Wang et al. 06] proposes an SVM based approach to hierarchical relation extraction, using
features derived automatically from a number of GATE-based open-source language
processing tools.
2005
[Aswani et al. 05] (Proceedings of Fifth International Conference on Recent Advances in
Natural Language Processing (RANLP2005)) It is a full-featured annotation indexing
and search engine, developed as a part of the GATE. It is powered with Apache Lucene
technology and indexes a variety of documents supported by the GATE.
[Bontcheva 05] presents the ONTOSUM system which uses Natural Language Generation
(NLG) techniques to produce textual summaries from Semantic Web ontologies.
[Cunningham 05] is an overview of the field of Information Extraction for the 2nd Edition
of the Encyclopaedia of Language and Linguistics.
[Cunningham & Bontcheva 05] is an overview of the field of Software Architecture for
Language Engineering for the 2nd Edition of the Encyclopaedia of Language and Linguistics.
[Dowman et al. 05a] (Euro Interactive Television Conference Paper) A system which can
use material from the Internet to augment television news broadcasts.
[Dowman et al. 05b] (World Wide Web Conference Paper) The Web is used to assist the
annotation and indexing of broadcast news.
22
Introduction
[Dowman et al. 05c] (Second European Semantic Web Conference Paper) A system that
semantically annotates television news broadcasts using news websites as a resource to
aid in the annotation process.
[Li et al. 05a] (Proceedings of Sheffield Machine Learning Workshop) describe an SVM
based IE system which uses the SVM with uneven margins as learning component and
the GATE as NLP processing module.
[Li et al. 05b] (Proceedings of Ninth Conference on Computational Natural Language
Learning (CoNLL-2005)) uses the uneven margins versions of two popular learning
algorithms SVM and Perceptron for IE to deal with the imbalanced classification problems derived from IE.
[Li et al. 05c] (Proceedings of Fourth SIGHAN Workshop on Chinese Language processing
(Sighan-05)) a system for Chinese word segmentation based on Perceptron learning, a
simple, fast and effective learning algorithm.
[Polajnar et al. 05] (University of Sheffield-Research Memorandum CS-05-10) UserFriendly Ontology Authoring Using a Controlled Language.
[Saggion & Gaizauskas 05] describes experiments on content selection for producing biographical summaries from multiple documents.
[Ursu et al. 05] (Proceedings of the 2nd European Workshop on the Integration of Knowledge, Semantic and Digital Media Technologies (EWIMT 2005))Digital Media Preservation and Access through Semantically Enhanced Web-Annotation.
[Wang et al. 05] (Proceedings of the 2005 IEEE/WIC/ACM International Conference on
Web Intelligence (WI 2005)) Extracting a Domain Ontology from Linguistic Resource
Based on Relatedness Measurements.
2004
[Bontcheva 04] (LREC 2004) describes lexical and ontological resources in GATE used for
Natural Language Generation.
[Bontcheva et al. 04] (JNLE) discusses developments in GATE in the early naughties.
[Cunningham & Scott 04a] (JNLE) is the introduction to the above collection.
[Cunningham & Scott 04b] (JNLE) is a collection of papers covering many important
areas of Software Architecture for Language Engineering.
[Dimitrov et al. 04] (Anaphora Processing) gives a lightweight method for named entity
coreference resolution.
[Li et al. 04] (Machine Learning Workshop 2004) describes an SVM based learning algorithm for IE using GATE.
Introduction
23
[Maynard et al. 04a] (LREC 2004) presents algorithms for the automatic induction of
gazetteer lists from multi-language data.
[Maynard et al. 04b] (ESWS 2004) discusses ontology-based IE in the hTechSight project.
[Maynard et al. 04c] (AIMSA 2004) presents automatic creation and monitoring of semantic metadata in a dynamic knowledge portal.
[Saggion & Gaizauskas 04a] describes an approach to mining definitions.
[Saggion & Gaizauskas 04b] describes a sentence extraction system that produces two
sorts of multi-document summaries; a general-purpose summary of a cluster of related
documents and an entity-based summary of documents related to a particular person.
[Wood et al. 04] (NLDB 2004) looks at ontology-based IE from parallel texts.
2003
[Bontcheva et al. 03] (NLPXML-2003) looks at GATE for the semantic web.
[Cunningham et al. 03] (Corpus Linguistics 2003) describes GATE as a tool for collaborative corpus annotation.
[Kiryakov 03] (Technical Report) discusses semantic web technology in the context of multimedia indexing and search.
[Manov et al. 03] (HLT-NAACL 2003) describes experiments with geographic knowledge
for IE.
[Maynard et al. 03a] (EACL 2003) looks at the distinction between information and content extraction.
[Maynard et al. 03c] (Recent Advances in Natural Language Processing 2003) looks at
semantics and named-entity extraction.
[Maynard et al. 03e] (ACL Workshop 2003) describes NE extraction without training
data on a language you don’t speak (!).
[Saggion et al. 03a] (EACL 2003) discusses robust, generic and query-based summarisation.
[Saggion et al. 03b] (Data and Knowledge Engineering) discusses multimedia indexing
and search from multisource multilingual data.
[Saggion et al. 03c] (EACL 2003) discusses event co-reference in the MUMIS project.
[Tablan et al. 03] (HLT-NAACL 2003) presents the OLLIE on-line learning for IE system.
24
Introduction
[Wood et al. 03] (Recent Advances in Natural Language Processing 2003) discusses using
parallel texts to improve IE recall.
2002
[Baker et al. 02] (LREC 2002) report results from the EMILLE Indic languages corpus
collection and processing project.
[Bontcheva et al. 02a] (ACl 2002 Workshop) describes how GATE can be used as an environment for teaching NLP, with examples of and ideas for future student projects
developed within GATE.
[Bontcheva et al. 02b] (NLIS 2002) discusses how GATE can be used to create HLT modules for use in information systems.
[Bontcheva et al. 02c], [Dimitrov 02a] and [Dimitrov 02b] (TALN 2002, DAARC
2002, MSc thesis) describe the shallow named entity coreference modules in GATE:
the orthomatcher which resolves pronominal coreference, and the pronoun resolution
module.
[Cunningham 02] (Computers and the Humanities) describes the philosophy and motivation behind the system, describes GATE version 1 and how well it lived up to its
design brief.
[Cunningham et al. 02] (ACL 2002) describes the GATE framework and graphical development environment as a tool for robust NLP applications.
[Dimitrov 02a, Dimitrov et al. 02] (DAARC 2002, MSc thesis) discuss lightweight coreference methods.
[Lal 02] (Master Thesis) looks at text summarisation using GATE.
[Lal & Ruger 02] (ACL 2002) looks at text summarisation using GATE.
[Maynard et al. 02a] (ACL 2002 Summarisation Workshop) describes using GATE to
build a portable IE-based summarisation system in the domain of health and safety.
[Maynard et al. 02c] (AIMSA 2002) describes the adaptation of the core ANNIE modules
within GATE to the ACE (Automatic Content Extraction) tasks.
[Maynard et al. 02d] (Nordic Language Technology) describes various Named Entity
recognition projects developed at Sheffield using GATE.
[Maynard et al. 02e] (JNLE) describes robustness and predictability in LE systems, and
presents GATE as an example of a system which contributes to robustness and to low
overhead systems development.
Introduction
25
[Pastra et al. 02] (LREC 2002) discusses the feasibility of grammar reuse in applications
using ANNIE modules.
[Saggion et al. 02b] and [Saggion et al. 02a] (LREC 2002, SPLPT 2002) describes how
ANNIE modules have been adapted to extract information for indexing multimedia
material.
[Tablan et al. 02] (LREC 2002) describes GATE’s enhanced Unicode support.
Older than 2002
[Maynard et al. 01] (RANLP 2001) discusses a project using ANNIE for named-entity
recognition across wide varieties of text type and genre.
[Bontcheva et al. 00] and [Brugman et al. 99] (COLING 2000, technical report) describe a prototype of GATE version 2 that integrated with the EUDICO multimedia
markup tool from the Max Planck Institute.
[Cunningham 00] (PhD thesis) defines the field of Software Architecture for Language
Engineering, reviews previous work in the area, presents a requirements analysis for
such systems (which was used as the basis for designing GATE versions 2 and 3), and
evaluates the strengths and weaknesses of GATE version 1.
[Cunningham et al. 00a], [Cunningham et al. 98a] and [Peters et al. 98] (OntoLex
2000, LREC 1998) presents GATE’s model of Language Resources, their access and
distribution.
[Cunningham et al. 00b] (LREC 2000) taxonomises Language Engineering components
and discusses the requirements analysis for GATE version 2.
[Cunningham et al. 00c] and [Cunningham et al. 99] (COLING 2000, AISB 1999)
summarise experiences with GATE version 1.
[Cunningham et al. 00d] and [Cunningham 99b] (technical reports) document early
versions of JAPE (superseded by the present document).
[Gambäck & Olsson 00] (LREC 2000) discusses experiences in the Svensk project, which
used GATE version 1 to develop a reusable toolbox of Swedish language processing
components.
[Maynard et al. 00] (technical report) surveys users of GATE up to mid-2000.
[McEnery et al. 00] (Vivek) presents the EMILLE project in the context of which GATE’s
Unicode support for Indic languages has been developed.
[Cunningham 99a] (JNLE) reviewed and synthesised definitions of Language Engineering.
26
Introduction
[Stevenson et al. 98] and [Cunningham et al. 98b] (ECAI 1998, NeMLaP 1998) report work on implementing a word sense tagger in GATE version 1.
[Cunningham et al. 97b] (ANLP 1997) presents motivation for GATE and GATE-like
infrastructural systems for Language Engineering.
[Cunningham et al. 96a] (manual) was the guide to developing CREOLE components for
GATE version 1.
[Cunningham et al. 96b] (TIPSTER) discusses a selection of projects in Sheffield using
GATE version 1 and the TIPSTER architecture it implemented.
[Cunningham et al. 96c, Cunningham et al. 96d, Cunningham et al. 95] (COLING
1996, AISB Workshop 1996, technical report) report early work on GATE version 1.
[Gaizauskas et al. 96a] (manual) was the user guide for GATE version 1.
[Gaizauskas et al. 96b, Cunningham et al. 97a, Cunningham et al. 96e] (ICTAI
1996, TIPSTER 1997, NeMLaP 1996) report work on GATE version 1.
[Humphreys et al. 96] (manual) describes the language processing components distributed with GATE version 1.
[Cunningham 94, Cunningham et al. 94] (NeMLaP 1994, technical report) argue that
software engineering issues such as reuse, and framework construction, are important
for language processing R&D.
Chapter 2
Installing and Running GATE
2.1
Downloading GATE
To download GATE point your web browser at http://gate.ac.uk/download/.
2.2
Installing and Running GATE
GATE will run anywhere that supports Java 6 or later, including Solaris, Linux, Mac OS
X and Windows platforms. We don’t run tests on other platforms, but have had reports of
successful installs elsewhere.
2.2.1
The Easy Way
The easy way to install is to use one of the platform-specific installers (created using the
excellent IzPack). Download a ‘platform-specific installer’ and follow the instructions it
gives you. Once the installation is complete, you can start GATE Developer using gate.exe
(Windows) or GATE.app (Mac) in the top-level installation directory, on Linux and other
platforms use gate.sh in the bin directory (see section 2.2.4).
Note for Mac users: on 64-bit-capable systems, GATE.app will run as a 64-bit application.
It will use the first listed 64-bit JVM in your Java Preferences, even if your highest priority
JVM is a 32-bit one.
27
28
Installing and Running GATE
2.2.2
The Hard Way (1)
Download the Java-only release package or the binary build snapshot, and follow the instructions below.
Prerequisites:
• A conforming Java 2 environment,
– version 1.4.2 or above for GATE 3.1
– version 5.0 for GATE 4.0 beta 1 or later.
– version 6.0 for GATE 6.1 or later.
available free from Sun Microsystems or from your UNIX supplier. (We test on various
Sun JDKs on Solaris, Linux and Windows XP.)
• Binaries from the GATE distribution you downloaded: gate.jar, lib/ext/guk.jar
(Unicode editing support) and a suitable script to start Ant, e.g. ant.sh or ant.bat.
These are held in a directory called bin like this:
.../bin/
gate.jar
ant.sh
ant.bat
You will also need the lib directory, containing various libraries that GATE depends
on.
• An open mind and a sense of humour.
Using the binary distribution:
• Unpack the distribution, creating a directory containing jar files and scripts.
• To run GATE Developer: on Windows, start a Command Prompt window, change
to the directory where you unpacked the GATE distribution and run ‘bin/ant.bat
run’; on UNIX/Linux or Mac open a terminal window and run ‘bin/ant run’. On
UNIX/Linux system you can alternately also use the bin/gate.sh script (see section 2.2.4).
• To embed GATE as a library (GATE Embedded), put gate.jar and all the libraries
in the lib directory in your CLASSPATH and tell Java that guk.jar is an extension
(-Djava.ext.dirs=path-to-guk.jar).
Installing and Running GATE
29
The Ant scripts that start GATE Developer (ant.bat or ant) require you to set the
JAVA HOME environment variable to point to the top level directory of your JAVA installation. The value of GATE CONFIG is passed to the system by the scripts using either a -i
command-line option, or the Java property gate.config.
2.2.3
The Hard Way (2): Subversion
The GATE code is maintained in a Subversion repository. You can use a Subversion client
to check out the source code – the most up-to-date version of GATE is the trunk:
svn checkout https://gate.svn.sourceforge.net/svnroot/gate/gate/trunk gate
Once you have checked out the code you can build GATE using Ant (see Section 2.5)
You can browse the complete Subversion repository online at
http://gate.svn.sourceforge.net/.
2.2.4
Running GATE Developer on Unix/Linux
The script gate.sh in the directory bin of your installation can be used to start GATE
Developer. You can run this script by entering its full path in a terminal or by adding the
bin directory to your binary path. In addition you can also add a symbolic link to this script
in any directory that already is in your binary path.
If gate.sh is invoked without parameters, GATE Developer will use the files ~/.gate.xml
and ~/.gate.session to store session and configuration data. Alternately you can run
gate.sh with the following parameters:
-h show usage information
-ld create or use the files .gate.session and .gate.xml in the current directory as the
session and configuration files.
-ln name create or use name.session and name.xml in the current directory as the session
and configuration files.
-ll if the current directory contains a file named log4j.properties then use it instead of the
default (GATE_HOME/bin/log4j.properties) to configure logging. Alternately, you
can specify any log4j configuration file by setting the log4j.configuration property
explicitly (see below).
all other parameters are passed on to the ant command. This can be used to
e.g. set properties using the ant option -D. For example to set the maximum
amount of heap memory to be used when running GATE to 6000M, you can add
30
Installing and Running GATE
-Druntime.max.memory=6000m as a parameter. The name of any property that should
get passed on to GATE should be prepended with ’run.’. In order to change the
default encoding used by GATE to UTF-8 add -Drun.file.encoding=utf-8 as a parameter. To specify a log4j configuration file add something like
-Drun.log4j.configuration=file:///home/myuser/log4jconfig.properties.
Running GATE Developer with either the -ld or the -ln option from different directories is
useful to keep several projects separate and can be used to run multiple instances of GATE
Developer (or even different versions of GATE Developer) in succession or even simultanously
without the configuration files getting mixed up between them.
2.3
Using System Properties with GATE
During initialisation, GATE reads several Java system properties in order to decide where
to find its configuration files.
Here is a list of the properties used, their default values and their meanings:
gate.home sets the location of the GATE install directory. This should point to the top
level directory of your GATE installation. This is the only property that is required.
If this is not set, the system will display an error message and them it will attempt to
guess the correct value.
gate.plugins.home points to the location of the directory containing installed plugins (a.k.a. CREOLE directories). If this is not set then the default value of
{gate.home}/plugins is used.
gate.site.config points to the location of the configuration file containing the site-wide
options. If not set this will default to {gate.home}/gate.xml. The site configuration
file must exist!
gate.user.config points to the file containing the user’s options. If not specified, or if the
specified file does not exist at startup time, the default value of gate.xml (.gate.xml on
Unix platforms) in the user’s home directory is used.
gate.user.session points to the file containing the user’s saved session. If not specified,
the default value of gate.session (.gate.session on Unix) in the user’s home directory
is used. When starting up GATE Developer, the session is reloaded from this file if it
exists, and when exiting GATE Developer the session is saved to this file (unless the
user has disabled ‘save session on exit’ in the configuration dialog). The session is not
used when using GATE Embedded.
gate.user.filechooser.defaultdir sets the default directory to be shown in the file chooser
of GATE Developer to the specified directory instead of the user’s operating-system
specific default directory.
Installing and Running GATE
31
load.plugin.path is a path-like structure, i.e. a list of URLs separated by ‘;’. All directories
listed here will be loaded as CREOLE plugins during initialisation. This has similar
functionality with the the -d command line option.
gate.builtin.creole.dir is a URL pointing to the location of GATE’s built-in CREOLE
directory. This is the location of the creole.xml file that defines the fundamental
GATE resource types, such as documents, document format handlers, controllers and
the basic visual resources that make up GATE. The default points to a location inside
gate.jar and should not generally need to be overridden.
When using GATE Embedded, you can set the values for these properties before you call
Gate.init(). Alternatively, you can set the values programmatically using the static
methods setGateHome(), setPluginsHome(), setSiteConfigFile(), etc. before calling
Gate.init(). See the Javadoc documentation for details. If you want to set these values
from the command line you can use the following syntax for setting gate.home for example:
java -Dgate.home=/my/new/gate/home/directory -cp...
gate.Main
To set these values when running GATE Developer from the command line via ant, of via
the gate.sh script, use the following syntax, prepending ‘run.’ to the property name, for
example to set gate.user.config:
./bin/ant run -Drun.gate.user.config=/my/path/to/user/config.file
or
./bin/gate.sh -Drun.gate.user.config=/my/path/to/user/config.file
Note that gate.home and some other properties are automatically set by build.xml and
cannot be overwritten on the command line when using either ant run or gate.sh.
When running GATE Developer, you can set the properties by creating a file
build.properties in the top level GATE directory. In this file, any system properties
which are prefixed with ‘run.’ will be passed to GATE. For example, to set an alternative
user config file, put the following line in build.properties1 :
run.gate.user.config=${user.home}/alternative-gate.xml
This facility is not limited to the GATE-specific properties listed above, for example the
following line changes the default temporary directory for GATE (note the use of forward
slashes, even on Windows platforms):
run.java.io.tmpdir=d:/bigtmp
1
In this specific case, the alternative config file must already exist when GATE starts up, so you should
copy your standard gate.xml file to the new location.
32
Installing and Running GATE
2.4
Configuring GATE
When GATE Developer is started, or when Gate.init() is called from GATE Embedded,
GATE loads various sorts of configuration data stored as XML in files generally called
something like gate.xml or .gate.xml. This data holds information such as:
• whether to save settings on exit;
• whether to save session on exit;
• what fonts GATE Developer should use;
• plugins to load at start;
• colours of the annotations;
• locations of files for the file chooser;
• and a lot of other GUI related options;
This type of data is stored at two levels (in order from general to specific):
• the site-wide level, which by default is located the gate.xml file in top level directory
of the GATE installation (i.e. the GATE home. This location can be overridden by the
Java system property gate.site.config;
• the user level, which lives in the user’s HOME directory on UNIX or their profile
directory on Windows (note that parts of this file are overwritten when saving user
settings). The default location for this file can be overridden by the Java system
property gate.user.config.
Where configuration data appears on several different levels, the more specific ones overwrite
the more general. This means that you can set defaults for all GATE users on your system,
for example, and allow individual users to override those defaults without interfering with
others.
Configuration data can be set from the GATE Developer GUI via the ‘Options’ menu then
‘Configuration’. The user can change the appearance of the GUI in the ‘Appearance’ tab,
which includes the options of font and the ‘look and feel’. The ‘Advanced’ tab enables the
user to include annotation features when saving the document and preserving its format, to
save the selected Options automatically on exit, and to save the session automatically on
exit. The ‘Input Methods’ submenu from the ‘Options’ menu enables the user to change the
default language for input. These options are all stored in the user’s .gate.xml file.
When using GATE Embedded, you can also set the site config location using
Gate.setSiteConfigFile(File) prior to calling Gate.init().
Installing and Running GATE
2.5
33
Building GATE
Note that you don’t need to build GATE unless you’re doing development on the system
itself.
Prerequisites:
• A conforming Java environment as above.
• A copy of the GATE sources and the build scripts – either the SRC distribution package
from the nightly snapshots or a copy of the code obtained through Subversion (see
Section 2.2.3).
• An appreciation of natural beauty.
GATE now includes a copy of the ANT build tool which can be accessed through the scripts
included in the bin directory (use ant.bat for Windows 98 or ME, ant.cmd for Windows
NT, 2000 or XP, and ant.sh for Unix platforms).
To build gate, cd to gate and:
1. Type:
bin/ant
2. [optional] To test the system:
bin/ant test
3. [optional] To make the Javadoc documentation:
bin/ant doc
4. You can also run GATE Developer using Ant, by typing:
bin/ant run
5. To see a full list of options type: bin/ant help
(The details of the build process are all specified by the build.xml file in the gate directory.)
You can also use a development environment like Borland JBuilder (click on the gate.jpx
file), but note that it’s still advisable to use ant to generate documentation, the jar file and so
on. Also note that the run configurations have the location of a gate.xml site configuration
file hard-coded into them, so you may need to change these for your site.
34
2.5.1
Installing and Running GATE
Using GATE with Maven/Ivy
This section is based on contributions by Marin Nozhchev (Ontotext) and Benson Margulies
(Basis Technology Corp).
Stable releases of GATE (since 5.2.1) are available in the standard central Maven repository,
with group ID “uk.ac.gate” and artifact ID “gate-core”. To use GATE in a Maven-based
project you can simply add a dependency:
<dependency>
<groupId>uk.ac.gate</groupId>
<artifactId>gate-core</artifactId>
<version>6.0</version>
</dependency>
Similarly, with a project that uses Ivy for dependency management:
<dependency org="uk.ac.gate" name="gate-core" rev="6.0"/>
The “gate-core” dependency brings in just gate.jar and its minimal dependencies, sufficient
to initialize GATE, load and save documents and corpora as XML, etc. The POM lists many
other dependencies which are marked as optional, so you can pick and choose which parts
of GATE you wish to depend on. In addition you will require the matching versions of any
GATE plugins you wish to use in your application – these are not managed by Maven or
Ivy, but can be obtained from the standard GATE release download.
2.6
Uninstalling GATE
If you have used the installer, run:
java -jar uninstaller.jar
or just delete the whole of the installation directory (the one containing bin, lib, Uninstaller,
etc.). The installer doesn’t install anything outside this directory, but for completeness you
might also want to delete the settings files GATE creates in your home directory (.gate.xml
and .gate.session).
Installing and Running GATE
2.7
2.7.1
35
Troubleshooting
I don’t see the Java console messages under Windows
Note that the gate.bat script uses javaw.exe to run GATE which means that you will see
no console for the java process. If you have problems starting GATE and you would like to
be able to see the console to check for messages then you should edit the gate.bat script
and replace javaw.exe with java.exe in the definition of the JAVA environment variable.
2.7.2
When I execute GATE, nothing happens
You might get some clues if you start GATE from the command line, using:
bin/ant -Druntime.spawn=false run
which will allow you to see all error messages GATE generates.
2.7.3
On Ubuntu, GATE is very slow or doesn’t start
GATE and many other Java applications are known not to work with GCJ, the open-source
Java SDK or others non SUN Java SDK.
Make sure you have the official version of Java installed. Provided by Sun, the package is
named ‘sun-java6-jdk’ in Synaptic.
To install it, run in a terminal:
sudo apt-get install sun-java6-jdk
Make sure that your default Java version is the one from SUN. You can do this by running:
sudo update-java-alternatives -l
This will list the installed Java VMs. You should see ‘java-6-sun’ as one of the options.
Then you should run :
sudo update-java-alternatives -s java-6-sun
to set the ‘java-6-sun’ as your default.
Finally, try GATE again.
36
2.7.4
Installing and Running GATE
How to use GATE on a 64 bit system?
32-bit vs. 64-bit is a matter of the JVM rather than the build of GATE For example, on Mac OS X, either use Applications/Utilities/Java Preferences and put one
of the 64-bit options at the top of the list, or run GATE from the terminal using Java 1.6.0
(which is 64-bit only on Mac OS):
export JAVA_HOME=
/System/Library/Frameworks/JavaVM.framework/Versions/1.6.0/Home
bin/ant run
2.7.5
I got the error: Could not reserve enough space for object
heap
GATE doesn’t use the JAVA OPTS variable. The default memory allocations are defined in
the gate/build.xml file but you can override them by creating a file called build.properties
in the same directory containing
runtime.start.memory=256m
runtime.max.memory=1048m
If you start GATE by running bin/ant run you can also overwrite these properties by using
the ant command line option -D, e.g.:
./bin/ant run -Druntime.start.memory=256m -Druntime.max.memory=1048m
On Linux, when using the bin/gate.sh command or a link to it to start GATE (see 2.2.4),
you can add these parameters to the command line arguments using the -D option, e.g.:
gate.sh -Druntime.start.memory=256m -Druntime.max.memory=1048m
If you don’t use ant to start GATE but your own application directly with the ‘java’ executable then you must use something like:
java -Xmx512m -classpath <your classpath here> <yourClassName>
Installing and Running GATE
2.7.6
37
From Eclipse, I got the error: java.lang.OutOfMemoryError:
Java heap space
Configuring xms and xmx parameters in eclipse.ini file just adds memory to your Eclipse
process. If you start a Java application from within Eclipse, that will run in a different
process.
To give more memory to your application, as opposed to just to Eclipse, you need to add
those values in the ‘VM Arguments’ section of the run application dialog: lower pane, in the
second tab of ‘Run Configurations’ dialog.
2.7.7
On MacOS, I got the error: java.lang.OutOfMemoryError:
Java heap space
You can try to set the environment variable ANT OPTS to allow for more memory as follows:
export ANT_OPTS=-Xmx1024m
2.7.8
I got the error: log4j:WARN No appenders could be found
for logger...
This may occur if you start GATE Developer or an application using GATE Embedded in a way so that the directory ’GATE_HOME/bin’ is not in the Java classpath
and you did not specify a valid URL for the system property log4j.configuration
either. The log4j logging component uses either the file whose URL is specified
with the property log4j.configuration or a file named log4j.properties from the
classpath for configuration. A quick fix for this problem is usually to copy the file
’GATE_HOME/bin/log4j.properties’ file to a directory which is part of the classpath of
your project.
2.7.9
Text is incorrectly refreshed after scrolling and become unreadable
Change the look and feel used in GATE with menu ‘Options’ then ‘Configuration’. Restart
GATE and try again. We use mainly ‘Metal’ and ‘Nimbus’ without problem.
Change the video driver you use.
Update Java.
38
2.7.10
Installing and Running GATE
An error occurred when running the TreeTagger plugin
The TreeTagger plugin isn’t supported anymore. However, the TaggerFramework plugin
provides support for TreeTagger. Try using that plugin instead. See section 20.3.
2.7.11
I got the error: HighlightData cannot be cast to ...HighlightInfo
That’s a recurring problem when editing a document with annotation highlights showing
and it usually involves inserting/deleting some text close or belonging to an annotation in
the first place.
The current solutions are to hide the annotation highlights before to edit the text or use
document read-only mode so you can only edit annotations or hide then show again the
document after the error.
Chapter 3
Using GATE Developer
‘The law of evolution is that the strongest survives!’
‘Yes; and the strongest, in the existence of any social species, are those who are
most social. In human terms, most ethical. . . . There is no strength to be gained
from hurting one another. Only weakness.’
The Dispossessed [p.183], Ursula K. le Guin, 1974.
This chapter introduces GATE Developer, which is the GATE graphical user interface. It is
analogous to systems like Mathematica for mathematicians, or Eclipse for Java programmers,
providing a convenient graphical environment for research and development of language
processing software. As well as being a powerful research tool in its own right, it is also very
useful in conjunction with GATE Embedded (the GATE API by which GATE functionality
can be included in your own applications); for example, GATE Developer can be used to
create applications that can then be embedded via the API. This chapter describes how
to complete common tasks using GATE Developer. It is intended to provide a good entry
point to GATE functionality, and so explanations are given assuming only basic knowledge
of GATE. However, probably the best way to learn how to use GATE Developer is to use
this chapter in conjunction with the demonstrations and tutorials movies. There are specific
links to them throughout the chapter. There is also a complete new set of video tutorials
here.
The basic business of GATE is annotating documents, and all the functionality we will
introduce relates to that. Core concepts are;
• the documents to be annotated,
• corpora comprising sets of documents, grouping documents for the purpose of running
uniform processes across them,
• annotations that are created on documents,
39
40
Using GATE Developer
• annotation types such as ‘Name’ or ‘Date’,
• annotation sets comprising groups of annotations,
• processing resources that manipulate and create annotations on documents, and
• applications, comprising sequences of processing resources, that can be applied to a
document or corpus.
What is considered to be the end result of the process varies depending on the task, but
for the purposes of this chapter, output takes the form of the annotated document/corpus.
Researchers might be more interested in figures demonstrating how successfully their application compares to a ‘gold standard’ annotation set; Chapter 10 in Part II will cover ways of
comparing annotation sets to each other and obtaining measures such as F1. Implementers
might be more interested in using the annotations programmatically; Chapter 7, also in Part
II, talks about working with annotations from GATE Embedded. For the purposes of this
chapter, however, we will focus only on creating the annotated documents themselves, and
creating GATE applications for future use.
GATE includes a complete information extraction system that you are free to use, called
ANNIE (a Nearly-New Information Extraction System). Many users find this is a good
starting point for their own application, and so we will cover it in this chapter. Chapter 6
talks in a lot more detail about the inner workings of ANNIE, but we aim to get you started
using ANNIE from inside of GATE Developer in this chapter.
We start the chapter with an exploration of the GATE Developer GUI, in Section 3.1. We
describe how to create documents (Section 3.2) and corpora (Section 3.3). We talk about
viewing and manually creating annotations (Section 3.4).
We then talk about loading the plugins that contain the processing resources you will use
to construct your application, in Section 3.5. We then talk about instantiating processing
resources (Section 3.6). Section 3.7 covers applications, including using ANNIE (Section
3.7.3). Saving applications and language resources (documents and corpora) is covered in
Section 3.8. We conclude with a few assorted topics that might be useful to the GATE
Developer user, in Section 3.10.
3.1
The GATE Developer Main Window
Figure 3.1 shows the main window of GATE Developer, as you will see it when you first run
it. There are five main areas:
1. at the top, the menus bar and tools bar with menus ‘File’, ‘Options’, ‘Tools’, ‘Help’
and icons for the most frequently used actions;
Using GATE Developer
41
Figure 3.1: Main Window of GATE Developer
2. on the left side, a tree starting from ‘GATE’ and containing ‘Applications’, ‘Language
Resources’ etc. – this is the resources tree;
3. in the bottom left corner, a rectangle, which is the small resource viewer;
4. in the center, containing tabs with ‘Messages’ or the name of a resource from the
resources tree, the main resource viewer;
5. at the bottom, the messages bar.
The menu and the messages bar do the usual things. Longer messages are displayed in the
messages tab in the main resource viewer area.
The resource tree and resource viewer areas work together to allow the system to display
diverse resources in various ways. The many resources integrated with GATE can have either
a small view, a large view, or both.
At any time, the main viewer can also be used to display other information, such as messages,
by clicking on the appropriate tab at the top of the main window. If an error occurs in
processing, the messages tab will flash red, and an additional popup error message may also
occur.
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Using GATE Developer
In the options dialogue from the Options menu you can choose if you want to link the
selection in the resources tree and the selected main view.
3.2
Loading and Viewing Documents
Figure 3.2: Making a New Document
If you right-click on ‘Language Resources’ in the resources pane, select “New’ then ‘GATE
Document’, the window ‘Parameters for the new GATE Document’ will appear as shown in
figure 3.2. Here, you can specify the GATE document to be created. Required parameters
are indicated with a tick. The name of the document will be created for you if you do not
specify it. Enter the URL of your document or use the file browser to indicate the file you
wish to use for your document source. For example, you might use ‘http://gate.ac.uk’, or
browse to a text or XML file you have on disk. Click on ‘OK’ and a GATE document will
be created from the source you specified.
See also the movie for creating documents.
The document editor is contained in the central tabbed pane in GATE Developer. Doubleclick on your document in the resources pane to view the document editor. The document
editor consists of a top panel with buttons and icons that control the display of different
views and the search box. Initially, you will see just the text of your document, as shown in
figure 3.3. Click on ‘Annotation Sets’ and ‘Annotations List’ to view the annotation sets to
the right and the annotations list at the bottom. You will see a view similar to figure 3.4.
In place of the annotations list, you can also choose to see the annotations stack. In place of
the annotation sets, you can also choose to view the co-reference editor. More information
about this functionality is given in Section 3.4.
Several options can be set from the triangle icon at the top right corner.
Using GATE Developer
43
Figure 3.3: The Document Editor
With ‘Save Current Layout’ you store the way the different views are shown and the annotation types highlighted in the document. Then if you set ‘Restore Layout Automatically’
you will get the same views and annotation types each time you open a document.
Another setting make the document editor ‘Read-only’. If enabled, you won’t be able to edit
the text but you will still be able to edit annotations. It is useful to avoid to involuntarily
modify the original text.
Finally you can choose between ‘Insert Append’ and ‘Insert Prepend’. That setting is only
relevant when you’re inserting text at the very border of an annotation.
If you place the cursor at the start of an annotation, in one case the newly entered text will
become part of the annotation, in the other case it will stay outside. If you place the cursor
at the end of an annotation, the opposite will happen.
Let use this sentence: ‘This is an [annotation].’ with the square brackets [] denoting the
boundaries of the annotation. If we insert a ‘x’ just before the ‘a’ or just after the ‘n’ of
‘annotation’, here’s what we get:
Append
44
Using GATE Developer
• This is an x[annotation].
• This is an [annotationx].
Prepend
• This is an [xannotation].
• This is an [annotation]x.
Figure 3.4: The Document Editor with Annotation Sets and Annotations List
Text in a loaded document can be edited in the document viewer. The usual platform specific
cut, copy and paste keyboard shortcuts should also work, depending on your operating
system (e.g. CTRL-C, CTRL-V for Windows). The last icon, a magnifying glass, at the
top of the document editor is for searching in the document. To prevent the new annotation
windows popping up when a piece of text is selected, hold down the CTRL key. Alternatively,
you can hide the annotation sets view by clicking on its button at the top of the document
view; this will also cause the highlighted portions of the text to become un-highlighted.
See also Section 18.2.3 for the compound document editor.
Using GATE Developer
3.3
45
Creating and Viewing Corpora
You can create a new corpus in a similar manner to creating a new document; simply rightclick on ‘Language Resources’ in the resources pane, select ‘New’ then ‘GATE corpus’. A
brief dialogue box will appear in which you can optionally give a name for your corpus (if
you leave this blank, a corpus name will be created for you) and optionally add documents
to the corpus from those already loaded into GATE.
There are three ways of adding documents to a corpus:
1. When creating the corpus, clicking on the icon next to the “documentsList” input field
brings up a popup window with a list of the documents already loaded into GATE
Developer. This enables the user to add any documents to the corpus.
2. Alternatively, the corpus can be loaded first, and documents added later by double
clicking on the corpus and using the + and - icons to add or remove documents to the
corpus. Note that the documents must have been loaded into GATE Developer before
they can be added to the corpus.
3. Once loaded, the corpus can be populated by right clicking on the corpus and selecting
‘Populate’. With this method, documents do not have to have been previously loaded
into GATE Developer, as they will be loaded during the population process. If you
right-click on your corpus in the resources pane, you will see that you have the option
to ‘Populate’ the corpus. If you select this option, you will see a dialogue box in which
you can specify a directory in which GATE will search for documents. You can specify
the extensions allowable; for example, XML or TXT. This will restrict the corpus
population to only those documents with the extensions you wish to load. You can
choose whether to recurse through the directories contained within the target directory
or restrict the population to those documents contained in the top level directory. Click
on ‘OK’ to populate your corpus. This option provides a quick way to create a GATE
Corpus from a directory of documents.
Additionally, right-clicking on a loaded document in the tree and selecting the ‘New corpus
with this document’ option creates a new transient corpus named Corpus for document
name containing just this document.
See also the movie for creating and populating corpora.
Double click on your corpus in the resources pane to see the corpus editor, shown in figure 3.5.
You will see a list of the documents contained within the corpus.
In the top left of the corpus editor, plus and minus buttons allow you to add documents to
the corpus from those already loaded into GATE and remove documents from the corpus
(note that removing a document from a corpus does not remove it from GATE).
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Using GATE Developer
Figure 3.5: Corpus Editor
Up and down arrows at the top of the view allow you to reorder the documents in the corpus.
The rightmost button in the view opens the currently selected document in a document
editor.
At the bottom, you will see that tabs entitled ‘Initialisation Parameters’ and ‘Corpus Quality
Assurance’ are also available in addition to the corpus editor tab you are currently looking at.
Clicking on the ‘Initialisation Parameters’ tab allows you to view the initialisation parameters
for the corpus. The ‘Corpus Quality Assurance’ tab allows you to calculate agreement
measures between the annotations in your corpus. Agreement measures are discussed in
depth in Chapter 10. The use of corpus quality assurance is discussed in Section 10.3.
Using GATE Developer
3.4
47
Working with Annotations
In this section, we will talk in more detail about viewing annotations, as well as creating and
editing them manually. As discussed in at the start of the chapter, the main purpose of GATE
is annotating documents. Whilst applications can be used to annotate the documents entirely
automatically, annotation can also be done manually, e.g. by the user, or semi-automatically,
by running an application over the corpus and then correcting/adding new annotations
manually. Section 3.4.5 focuses on manual annotation. In Section 3.6 we talk about running
processing resources on our documents. We begin by outlining the functionality around
viewing annotations, organised by the GUI area to which the functionality pertains.
3.4.1
The Annotation Sets View
To view the annotation sets, click on the ‘Annotation Sets’ button at the top of the document
editor, or use the F3 key (see Section 3.9 for more keyboard shortcuts). This will bring up the
annotation sets viewer, which displays the annotation sets available and their corresponding
annotation types.
The annotation sets view is displayed on the left part of the document editor. It’s a tree-like
view with a root for each annotation set. The first annotation set in the list is always a
nameless set. This is the default annotation set. You can see in figure 3.4 that there is a
drop-down arrow with no name beside it. Other annotation sets on the document shown in
figure 3.4 are ‘Key’ and ‘Original markups’. Because the document is an XML document,
the original XML markup is retained in the form of an annotation set. This annotation set
is expanded, and you can see that there are annotations for ‘TEXT’, ‘body’, ‘font’, ‘html’,
‘p’, ‘table’, ‘td’ and ‘tr’.
To display all the annotations of one type, tick its checkbox or use the space key. The text
segments corresponding to these annotations will be highlighted in the main text window.
To delete an annotation type, use the delete key. To change the color, use the enter key.
There is a context menu for all these actions that you can display by right-clicking on one
annotation type, a selection or an annotation set.
If you keep shift key pressed when you open the annotation sets view, GATE Developer will
try to select any annotations that were selected in the previous document viewed (if any);
otherwise no annotation will be selected.
Having selected an annotation type in the annotation sets view, hovering over an annotation
in the main resource viewer or right-clicking on it will bring up a popup box containing a
list of the annotations associated with it, from which one can select an annotation to view
in the annotation editor, or if there is only one, the annotation editor for that annotation.
Figure 3.6 shows the annotation editor.
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Using GATE Developer
Figure 3.6: The Annotation Editor
3.4.2
The Annotations List View
To view the list of annotations and their features, click on the ‘Annotations list’ button
at the top of the main window or use F4 key. The annotation list view will appear below
the main text. It will only contain the annotations selected from the annotation sets view.
These lists can be sorted in ascending and descending order for any column, by clicking on
the corresponding column heading. Moreover you can hide a column by using the context
menu by right-clicking on the column headings. Selecting rows in the table will blink the
respective annotations in the document. Right-click on a row or selection in this view to
delete or edit an annotation. Delete key is a shortcut to delete selected annotations.
3.4.3
The Annotations Stack View
Figure 3.7: Annotations stack view centred on the document caret.
This view is similar to the ANNIC view described in section 9.2. It displays annotations
at the document caret position with some context before and after. The annotations are
stacked from top to bottom, which gives a clear view when they are overlapping.
Using GATE Developer
49
As the view is centred on the document caret, you can use the conventional keypresses to
move it and update the view: notably the keys left and right to skip one letter; control +
left/right to skip one word; up and down to go one line up or down; and use the document
scrollbar then click in the document to move further. There are also two buttons at the top
of the view that centre the view on the closest previous/next annotation boundary among
all displayed. This is useful when you want to skip a region without annotation or when you
want to reach the beginning or end of a very long annotation.
The annotation types displayed correspond to those selected in the annotation sets view. You
can display feature values for an annotation rectangle by hovering the mouse on it or select
only one feature to display by double-clicking on the annotation type in the first column.
Right-clicking on an annotation in the annotations stack view gives the option to edit that
annotation.
3.4.4
The Co-reference Editor
Figure 3.8: Co-reference editor inside a document editor. The popup window in the document under the word ‘EPSRC’ is used to add highlighted annotations to a co-reference
chain. Here the annotation type ‘Organization’ of the annotation set ‘Default’ is highlighted
and also the co-references ‘EC’ and ‘GATE’.
The co-reference editor allows co-reference chains (see Section 6.9) to be displayed and edited
in GATE Developer. To display the co-reference editor, first open a document in GATE
Developer, and then click on the Co-reference Editor button in the document viewer.
The combo box at the top of the co-reference editor allows you to choose which annotation
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Using GATE Developer
set to display co-references for. If an annotation set contains no co-reference data, then the
tree below the combo box will just show ‘Coreference Data’ and the name of the annotation
set. However, when co-reference data does exist, a list of all the co-reference chains that are
based on annotations in the currently selected set is displayed. The name of each co-reference
chain in this list is the same as the text of whichever element in the chain is the longest. It
is possible to highlight all the member annotations of any chain by selecting it in the list.
When a co-reference chain is selected, if the mouse is placed over one of its member annotations, then a pop-up box appears, giving the user the option of deleting the item from the
chain. If the only item in a chain is deleted, then the chain itself will cease to exist, and it
will be removed from the list of chains. If the name of the chain was derived from the item
that was deleted, then the chain will be given a new name based on the next longest item
in the chain.
A combo box near the top of the co-reference editor allows the user to select an annotation
type from the current set. When the Show button is selected all the annotations of the
selected type will be highlighted. Now when the mouse pointer is placed over one of those
annotations, a pop-up box will appear giving the user the option of adding the annotation
to a co-reference chain. The annotation can be added to an existing chain by typing the
name of the chain (as shown in the list on the right) in the pop-up box. Alternatively, if
the user presses the down cursor key, a list of all the existing annotations appears, together
with the option [New Chain]. Selecting the [New Chain] option will cause a new chain to
be created containing the selected annotation as its only element.
Each annotation can only be added to a single chain, but annotations of different types can
be added to the same chain, and the same text can appear in more than one chain if it is
referenced by two or more annotations.
The movie for inspecting results is also useful for learning about viewing annotations.
3.4.5
Creating and Editing Annotations
To create annotations manually, select the text you want to annotate and hover the mouse
on the selection or use control+E keys. A popup will appear, allowing you to create an
annotation, as shown in figure 3.9
Figure 3.9: Creating a New Annotation
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The type of the annotation, by default, will be the same as the last annotation you created,
unless there is none, in which case it will be ‘ New ’. You can enter any annotation type name
you wish in the text box, unless you are using schema-driven annotation (see Section 3.4.6).
You can add or change features and their values in the table below.
To delete an annotation, click on the red X icon at the top of the popup window. To
grow/shrink the span of the annotation at its start use the two arrow icons on the left or
right and left keys. Use the two arrow icons next on the right to change the annotation end
or alt+right and alt+left keys. Add shift and control+shift keys to make the span increment
bigger. The red X icon is for removing the annotation.
The pin icon is to pin the window so that it remains where it is. If you drag and drop the
window, this automatically pins it too. Pinning it means that even if you select another
annotation (by hovering over it in the main resource viewer) it will still stay in the same
position.
The popup menu only contains annotation types present in the Annotation Schema and
those already listed in the relevant Annotation Set. To create a new Annotation Schema,
see Section 3.4.6. The popup menu can be edited to add a new annotation type, however.
The new annotation created will automatically be placed in the annotation set that has been
selected (highlighted) by the user. To create a new annotation set, type the name of the new
set to be created in the box below the list of annotation sets, and click on ‘New’.
Figure 3.10 demonstrates adding a ‘Organization’ annotation for the string ‘EPSRC’ (highlighted in green) to the default annotation set (blank name in the annotation set view on
the right) and a feature name ‘type’ with a value about to be added.
To add a second annotation to a selected piece of text, or to add an overlapping annotation to
an existing one, press the CTRL key to avoid the existing annotation popup appearing, and
then select the text and create the new annotation. Again by default the last annotation type
to have been used will be displayed; change this to the new annotation type. When a piece
of text has more than one annotation associated with it, on mouseover all the annotations
will be displayed. Selecting one of them will bring up the relevant annotation popup.
To search and annotate the document automatically, use the search and annotate function
as shown in figure 3.11:
• Create and/or select an annotation to be used as a model to annotate.
• Open the panel at the bottom of the annotation editor window.
• Change the expression to search if necessary.
• Use the [First] button or Enter key to select the first expression to annotate.
• Use the [Annotate] button if the selection is correct otherwise the [Next] button. After
a few cycles of [Annotate] and [Next], Use the [Ann. all next] button.
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Figure 3.10: Adding an Organization annotation to the Default Annotation Set
Note that after using the [First] button you can move the caret in the document and use the
[Next] button to avoid continuing the search from the beginning of the document. The [?]
button at the end of the search text field will help you to build powerful regular expressions
to search.
3.4.6
Schema-Driven Editing
Annotation schemas allow annotation types and features to be pre-specified, so that during
manual annotation, the relevant options appear on the drop-down lists in the annotation
editor. You can see some example annotation schemas in Section 5.4.1. Annotation schemas
provide a means to define types of annotations in GATE Developer. Basically this means
that GATE Developer ‘knows about’ annotations defined in a schema.
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Figure 3.11: Search and Annotate Function of the Annotation Editor.
Annotation schemas are supported by the ‘Annotation schema’ language resource in ANNIE,
so to use them you must first ensure that the ‘ANNIE’ plugin is loaded (see Section 3.5).
This will load a set of default schemas, as well as allowing you to load schemas of your own.
The default annotation schemas contain common named entities such as Person, Organisation, Location, etc. You can modify the existing schema or create a new one, in order to tell
GATE Developer about other kinds of annotations you frequently use. You can still create
annotations in GATE Developer without having specified them in an annotation schema,
but you may then need to tell GATE Developer about the properties of that annotation
type each time you create an annotation for it.
To load a schema of your own, right-click on ‘Language Resources’ in the resources pane.
Select ‘New’ then ‘Annotation schema’. A popup box will appear in which you can browse
to your annotation schema XML file.
An alternative annotation editor component is available which constrains the available annotation types and features much more tightly, based on the annotation schemas that are
currently loaded. This is particularly useful when annotating large quantities of data or for
use by less skilled users.
To use this, you must load the Schema_Annotation_Editor plugin. With this plugin loaded,
the annotation editor will only offer the annotation types permitted by the currently loaded
set of schemas, and when you select an annotation type only the features permitted by the
schema are available to edit1 . Where a feature is declared as having an enumerated type the
available enumeration values are presented as an array of buttons, making it easy to select
the required value quickly.
1
existing features outwith the schema, e.g. those created by previously-run processing resources, are not
editable but not modified or removed by the editor.
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3.4.7
Using GATE Developer
Printing Text with Annotations
We suggest you to use your browser to print a document as GATE don’t propose a printing
facility for the moment.
First save your document by right clicking on the document in the left resources tree then
choose ‘Save Preserving Format’. You will get an XML file with all the annotations highlighted as XML tags plus the ‘Original markups’ annotations set.
It’s possible that the output will not have an XML header and footer because the document
was created from a plain text document. In that case you can use the XHTML example
below.
Then add a stylesheet processing instruction at the beginning of the XML file, the second
line in the following minimalist XHTML document:
<?xml version="1.0" encoding="UTF-8" ?>
<?xml-stylesheet type="text/css" href="gate.css"?>
<!DOCTYPE html
PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
<head>
<title>Virtual Library</title>
</head>
<body>
<p>Content of the document</p>
...
</body>
</html>
And create a file ‘gate.css’ in the same directory:
BODY, body { margin: 2em } /* or any other first level tag */
P, p { display: block } /* or any other paragraph tag */
/* ANNIE tags but you can use whatever tags you want */
/* be careful that XML tags are case sensitive */
Date
{ background-color: rgb(230, 150, 150) }
FirstPerson { background-color: rgb(150, 230, 150) }
Identifier
{ background-color: rgb(150, 150, 230) }
JobTitle
{ background-color: rgb(150, 230, 230) }
Location
{ background-color: rgb(230, 150, 230) }
Money
{ background-color: rgb(230, 230, 150) }
Organization { background-color: rgb(230, 200, 200) }
Percent
{ background-color: rgb(200, 230, 200) }
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Person
{ background-color: rgb(200, 200, 230) }
Title
{ background-color: rgb(200, 230, 230) }
Unknown
{ background-color: rgb(230, 200, 230) }
Etc
{ background-color: rgb(230, 230, 200) }
/* The next block is an example for having a small tag
with the name of the annotation type after each annotation */
Date:after {
content: "Date";
font-size: 50%;
vertical-align: sub;
color: rgb(100, 100, 100);
}
Finally open the XML file in your browser and print it.
Note that overlapping annotations, cannot be expressed correctly with inline XML tags and
thus won’t be displayed correctly.
3.5
Using CREOLE Plugins
In GATE, processing resources are used to automatically create and manipulate annotations
on documents. We will talk about processing resources in the next section. However, we
must first introduce CREOLE plugins. In most cases, in order to use a particular processing
resource (and certain language resources) you must first load the CREOLE plugin that
contains it. This section talks about using CREOLE plugins. Then, in Section 3.6, we will
talk about creating and using processing resources.
The definitions of CREOLE resources (e.g. processing resources such as taggers and parsers,
see Chapter 4) are stored in CREOLE directories (directories containing an XML file describing the resources, the Java archive with the compiled executable code and whatever
libraries are required by the resources).
Starting with version 3, CREOLE directories are called ‘CREOLE plugins’ or simply ‘plugins’. In previous versions, the CREOLE resources distributed with GATE used to be included
in the monolithic gate.jar archive. Version 3 includes them as separate directories under
the plugins directory of the distribution. This allows easy access to the linguistic resources
used without the requirement to unpack the gate.jar file.
Plugins can have one or more of the following states in relation with GATE:
known plugins are those plugins that the system knows about. These include all the plugins
in the plugins directory of the GATE installation (the so–called installed plugins) as
well all the plugins that were manually loaded from the user interface.
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loaded plugins are the plugins currently loaded in the system. All CREOLE resource types
from the loaded plugins are available for use. All known plugins can easily be loaded
and unloaded using the user interface.
auto-loadable plugins are the list of plugins that the system loads automatically during
initialisation.
The default location for installed plugins can be modified using the gate.plugins.home system property while the list of auto-loadable plugins can be set using the load.plugin.path
property, see Section 2.3 above.
The CREOLE plugins can be managed through the graphical user interface which can be
activated by selecting ‘Manage CREOLE Plugins’ from the ‘File’ menu. This will bring up
a window listing all the known plugins. For each plugin there are two check-boxes – one
labelled ‘Load now’, which will load the plugin, and the other labelled ‘Load always’ which
will add the plugin to the list of auto-loadable plugins. A ‘Delete’ button is also provided –
which will remove the plugin from the list of known plugins. This operation does not delete
the actual plugin directory. Installed plugins are found automatically when GATE is started;
if an installed plugin is deleted from the list, it will re-appear next time GATE is launched.
Figure 3.12: Plugin Management Console
If you select a plugin, you will see in the pane on the right the list of resources that plugin
contains. For example, in figure 3.12, the ‘Alignment’ plugin is selected, and you can see that
it contains ten processing resources; ‘Compound Document’, ‘Compound Document From
Xml’, ‘Compound Document Editor’, ‘GATE Composite document’ etc. If you wish to use
a particular resource you will have to ascertain which plugin contains it. This list can be
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useful for that. Alternatively, the GATE website provides a directory of plugins and their
processing resources.
Having loaded the plugins you need, the resources they define will be available for use.
Typically, to the GATE Developer user, this means that they will appear on the ‘New’ menu
when you right-click on ‘Processing Resources’ in the resources pane, although some special
plugins have different effects; for example, the Schema Annotation Editor (see Section 3.4.6).
3.6
Loading and Using Processing Resources
This section describes how to load and run CREOLE resources not present in ANNIE. To load
ANNIE, see Section 3.7.3. For technical descriptions of these resources, see the appropriate
chapter in Part III (e.g. Chapter 20). First ensure that the necessary plugins have been
loaded (see Section 3.5). If the resource you require does not appear in the list of Processing
Resources, then you probably do not have the necessary plugin loaded. Processing resources
are loaded by selecting them from the set of Processing Resources: right click on Processing
Resources or select ‘New Processing Resource’ from the File menu.
For example, use the Plugin Console Manager to load the ‘Tools’ plugin. When you right
click on ‘Processing Resources’ in the resources pane and select ‘New’ you have the option
to create any of the processing resources that plugin provides. You may choose to create a
‘GATE Morphological Analyser’, with the default parameters. Having done this, an instance
of the GATE Morphological Analyser appears under ‘Processing Resources’. This processing
resource, or PR, is now available to use. Double-clicking on it in the resources pane reveals
its initialisation parameters, see figure 3.13.
This processing resource is now available to be added to applications. It must be added to
an application before it can be applied to documents. You may create as many of a particular processing resource as you wish, for example with different initialisation parameters.
Section 3.7 talks about creating and running applications.
See also the movie for loading processing resources.
3.7
Creating and Running an Application
Once all the resources you need have been loaded, an application can be created from them,
and run on your corpus. Right click on ‘Applications’ and select ‘New’ and then either
‘Corpus Pipeline’ or ‘Pipeline’. A pipeline application can only be run over a single document,
while a corpus pipeline can be run over a whole corpus.
To build the pipeline, double click on it, and select the resources needed to run the application
(you may not necessarily wish to use all those which have been loaded).
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Figure 3.13: GATE Morphological Analyser Initialisation Parameters
Transfer the necessary components from the set of ‘loaded components’ displayed on the left
hand side of the main window to the set of ‘selected components’ on the right, by selecting
each component and clicking on the left and right arrows, or by double-clicking on each
component.
Ensure that the components selected are listed in the correct order for processing (starting
from the top). If not, select a component and move it up or down the list using the up/down
arrows at the left side of the pane.
Ensure that any parameters necessary are set for each processing resource (by clicking on
the resource from the list of selected resources and checking the relevant parameters from
the pane below). For example, if you wish to use annotation sets other than the Default one,
these must be defined for each processing resource.
Note that if a corpus pipeline is used, the corpus needs only to be set once, using the dropdown menu beside the ‘corpus’ box. If a pipeline is used, the document must be selected for
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each processing resource used.
Finally, click on ‘Run’ to run the application on the document or corpus.
See also the movie for loading and running processing resources.
For how to use the conditional versions of the pipelines see Section 3.7.2 and for saving/restoring the configuration of an application see Section 3.8.3.
3.7.1
Running an Application on a Datastore
To avoid loading all your documents at the same time you can run an application on a
datastore corpus.
To do this you need to load your datastore, see section 3.8.2, and to load the corpus from
the datastore by double clicking on it in the datastore viewer.
Then, in the application viewer, you need to select this corpus in the drop down list of
corpora.
When you run the application on the corpus datastore, each document will be loaded, processed, saved then unloaded. So at any time there will be only one document from the
datastore corpus loaded. This prevent memory shortage but is also a little bit slower than
if all your documents were already loaded.
The processed documents are automatically saved back to the datastore so you may want
to use a copy of the datastore to experiment.
Be very careful that if you have some documents from the datastore corpus already loaded
before running the application then they will not be unloaded nor saved. To save such
document you have to right click on it in the resources tree view and save it to the datastore.
3.7.2
Running PRs Conditionally on Document Features
The ‘Conditional Pipeline’ and ‘Conditional Corpus Pipeline’ application types are conditional versions of the pipelines mentioned in Section 3.7 and allow processing resources to
be run or not according to the value of a feature on the document. In terms of graphical
interface, the only addition brought by the conditional versions of the applications is a box
situated underneath the lists of available and selected resources which allows the user to
choose whether the currently selected processing resource will run always, never or only on
the documents that have a particular value for a named feature.
If the Yes option is selected then the corresponding resource will be run on all the documents
processed by the application as in the case of non-conditional applications. If the No option
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is selected then the corresponding resource will never be run; the application will simply
ignore its presence. This option can be used to temporarily and quickly disable an application
component, for debugging purposes for example.
The If value of feature option permits running specific application components conditionally
on document features. When selected, this option enables two text input fields that are used
to enter the name of a feature and the value of that feature for which the corresponding
processing resource will be run. When a conditional application is run over a document, for
each component that has an associated condition, the value of the named feature is checked
on the document and the component will only be used if the value entered by the user
matches the one contained in the document features.
At first sight the conditional behaviour available with these controller may seem limited, but
in fact it is very powerful when used in conjunction with JAPE grammars (see chapter 8).
Complex conditions can be encoded in JAPE rules which set the appropriate feature values
on the document for use by the conditional controllers. Alternatively, the Groovy plugin
provides a scriptable controller (see section 7.16.3) in which the execution strategy is defined
by a Groovy script, allowing much richer conditional behaviour to be encoded directly in the
controller’s configuration.
3.7.3
Doing Information Extraction with ANNIE
This section describes how to load and run ANNIE (see Chapter 6) from GATE Developer. ANNIE is a good place to start because it provides a complete information extraction
application, that you can run on any corpus. You can then view the effects.
From the File menu, select ‘Load ANNIE System’. To run it in its default state, choose
‘with Defaults’. This will automatically load all the ANNIE resources, and create a corpus
pipeline called ANNIE with the correct resources selected in the right order, and the default
input and output annotation sets.
If ‘without Defaults’ is selected, the same processing resources will be loaded, but a popup
window will appear for each resource, which enables the user to specify a name, location
and other parameters for the resource. This is exactly the same procedure as for loading a
processing resource individually, the difference being that the system automatically selects
those resources contained within ANNIE. When the resources have been loaded, a corpus
pipeline called ANNIE will be created as before.
The next step is to add a corpus (see Section 3.3), and select this corpus from the dropdown corpus menu in the Serial Application editor. Finally click on ‘Run’ from the Serial
Application editor, or by right clicking on the application name in the resources pane and
selecting ‘Run’. (Many people prefer to switch to the messages tab, then run their application
by right-clicking on it in the resources pane, because then it is possible to monitor any
messages that appear whilst the application is running.)
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To view the results, double click on one of the document contained in the corpus processed in
the left hand tree view. No annotation sets nor annotations will be shown until annotations
are selected in the annotation sets; the ‘Default’ set is indicated only with an unlabelled
right-arrowhead which must be selected in order to make visible the available annotations.
Open the default annotation set and select some of the annotations to see what the ANNIE
application has done.
See also the movie for loading and running ANNIE.
3.7.4
Modifying ANNIE
You will find the ANNIE resources in gate/plugins/ANNIE/resources. Simply locate the
existing resources you want to modify, make a copy with a new name, edit them, and load
the new resources into GATE as new Processing Resources (see Section 3.6).
3.8
Saving Applications and Language Resources
In this section, we will describe how applications and language resources can be saved for
use outside of GATE and for use with GATE at a later time. Section 3.8.1 talks about
saving documents to file. Section 3.8.2 outlines how to use datastores. Section 3.8.3 talks
about saving application states (resource parameter states), and Section 3.8.4 talks about
exporting applications together with referenced files and resources to a ZIP file.
3.8.1
Saving Documents to File
There are three main ways to save annotated documents:
1. preserving the original markup, with optional added annotations;
2. in GATE’s own XML serialisation format (including all the annotations on the document);
3. by writing your own dump algorithm as a processing resource.
This section describes how to use the first two options.
Both types of data export are available in the popup menu triggered by right-clicking on a
document in the resources tree (see Section 3.1): type 1 is called ‘Save Preserving Format’
and type 2 is called ‘Save as XML’. In addition, all documents in a corpus can be saved as
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individual XML files into a directory by right-clicking on the corpus in the resources tree
and choosing the option ‘Save as XML‘.
Selecting the save as XML option leads to a file open dialogue; give the name of the file you
want to create, and the whole document and all its data will be exported to that file. If you
later create a document from that file, the state will be restored. (Note: because GATE’s
annotation model is richer than that of XML, and because our XML dump implementation
sometimes cuts corners2 , the state may not be identical after restoration. If your intention
is to store the state for later use, use a DataStore instead.)
The ‘Save Preserving Format’ option also leads to a file dialogue; give a name and the data
you require will be dumped into the file. The action can be used for documents that were
created from files using the XML or HTML format. It will save all the original tags as well
as the document annotations that are currently displayed in the ‘Annotations List’ view.
This option is useful for selectively saving only some annotation types.
The annotations are saved as normal document tags, using the annotation type as the tag
name. If the advanced option ‘Include annotation features for “Save Preserving Format”’ (see
Section 2.4) is set to true, then the annotation features will also be saved as tag attributes.
Using this operation for GATE documents that were not created from an HTML or XML
file results in a plain text file, with in-line tags for the saved annotations.
Note that GATE’s model of annotation allows graph structures, which are difficult to represent in XML (XML is a tree-structured representation format). During the dump process,
annotations that cross each other in ways that cannot be represented in legal XML will be
discarded, and a warning message printed.
3.8.2
Saving and Restoring LRs in Datastores
Where corpora are large, the memory available may not be sufficient to have all documents
open simultaneously. The datastore functionality provides the option to save documents to
disk and open them only one at a time for processing. This means that much larger corpora
can be used. A datastore can also be useful for saving documents in an efficient and lossless
way.
To save a text in a datastore, a new datastore must first be created if one does not already
exist. Create a datastore by right clicking on Datastore in the left hand pane, and select the
option ‘Create Datastore’. Select the data store type you wish to use. Create a directory to
be used as the datastore (note that the datastore is a directory and not a file).
You can either save a whole corpus to the datastore (in which case the structure of the corpus
2
Gorey details: features of annotations and documents in GATE may be any virtually any Java object;
serialising arbitrary binary data to XML is not simple; instead we serialise them as strings, and therefore
they will be re-loaded as strings.
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will be preserved) or you can save individual documents. The recommended method is to
save the whole corpus. To save a corpus, right click on the corpus name and select the ‘Save
to...’ option (giving the name of the datastore created earlier). To save individual documents
to the datastore, right clicking on each document name and follow the same procedure.
To load a document from a datastore, do not try to load it as a language resource. Instead,
open the datastore by right clicking on Datastore in the left hand pane, select ‘Open Datastore’ and choose the datastore to open. The datastore tree will appear in the main window.
Double click on a corpus or document in this tree to open it. To save a corpus and document
back to the same datastore, simply select the ‘Save’ option.
See also the movie for creating a datastore and the movie for loading corpus and documents
from a datastore.
3.8.3
Saving Application States to a File
Resources, and applications that are made up of them, are created based on the settings of
their parameters (see Section 3.6). It is possible to save the data used to create an application
to a file and re-load it later. To save the application to a file, right click on it in the resources
tree and select ‘Save application state’, which will give you a file creation dialogue. Choose
a file name that ends in gapp as this file dialog and the one for loading application states age
displays all files which have a name ending in gapp. A common convention is to use .gapp
as a file extension.
To restore the application later, select ‘Restore application from file’ from the ‘File’ menu.
Note that the data that is saved represents how to recreate an application – not the resources
that make up the application itself. So, for example, if your application has a resource that
initialises itself from some file (e.g. a grammar, a document) then that file must still exist
when you restore the application.
In case you don’t want to save the corpus configuration associated with the application then
you must select ‘<none>’ in the corpus list of the application before saving the application.
The file resulting from saving the application state contains the values of the initialisation
and runtime parameters for all the processing resources contained by the stored application
as well as the values of the initialisation parameters for all the language resources referenced
by those processing resources. Note that if you reference a document that has been created
with an empty URL and empty string content parameter and subsequently been manually
edited to add content, that content will not be saved. In order for document content to be
preserved, load the document from an URL, specify the content as for the string content
parameter or use a document from a datastore.
For the parameters of type URL (which are typically used to select external resources such
as grammars or rules files) a transformation is applied so that the paths are stored relative
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to the location of the file used to store the state. This means that the resource files which
are not part of GATE and used by an application do not need to be in the same location
as when the application was initially created but rather in the same location relative to the
location of the application file. It also means that the resource files which are part of GATE
should be found correctly no matter where GATE is installed. This allows the creation and
deployment of portable applications by keeping the application file and the resource files
used by the application together.
If you want to save your application along with all the resources it requires you can use the
‘Export for GATECloud.net’ option (see Section 3.8.4).
See also the movie for saving and restoring applications.
3.8.4
Saving an Application with its Resources (e.g.
Cloud.net)
GATE-
When you save an application using the ‘Save application state’ option (see Section 3.8.3),
the saved file contains references to the plugins that were loaded when the application was
saved, and to any resource files required by the application. To be able to reload the file,
these plugins and other dependencies must exist at the same locations (relative to the saved
state file). While this is fine for saving and loading applications on a single machine it
means that if you want to package your application to run it elsewhere (e.g. deploy it to
GATECloud.net) then you need to be careful to include all the resource files and plugins
at the right locations in your package. The ‘Export for GATECloud.net’ option on the
right-click menu for an application helps to automate this process.
When you export an application in this way, GATE Developer produces a ZIP file containing
the saved application state (in the same format as ‘Save application state’). Any plugins and
resource files that the application refers to are also included in the zip file, and the relative
paths in the saved state are rewritten to point to the correct locations within the package.
The resulting package is therefore self-contained and can be copied to another machine and
unpacked there, or passed to GATECloud.net for deployment.
As well as selecting the location where you want to save the package, the ‘Export for GATECloud.net’ option will also prompt you to select the annotation sets that your application
uses for input and output. For example, if your application makes use of the unpacked XML
markup in source documents and creates annotations in the default set then you would select ‘Original markups’ as an input set and the ‘<Default annotation set>’ as an output
set. GATE Developer will try to make an educated guess at the correct sets but you should
check and amend the lists as necessary.
There are a few important points to note about the export process:
• The complete contents of all the plugin directories that are loaded when you perform
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the export will be included in the resulting package. Use the plugin manager to unload
any plugins your application is not using before you export it.
• If your application refers to a resource file in a directory that is not under one of the
loaded plugins, the entire contents of this directory will be recursively included in the
package. If you have a number of unrelated resources in a single directory (e.g. many
sets of large gazetteer lists) you may want to separate them into separate directories
so that only the relevant ones are included in the package.
• The packager only knows about resources that your application refers to directly in its
parameters. For example, if your application includes a multi-phase JAPE grammar
the packager will only consider the main grammar file, not any of its sub-phases. If
the sub-phases are not contained in the same directory as the main grammar you may
find they are not included. If indirect references of this kind are all to files under the
same directory as the ‘master’ file it will work OK.
If you require more flexibility than this option provides you should read Section E.2, which
describes the underlying Ant task that the exporter uses.
3.9
Keyboard Shortcuts
You can use various keyboard shortcuts for common tasks in GATE Developer. These are
listed in this section.
General (Section 3.1):
• F1 Display a help page for the selected component
• Alt+F4 Exit the application without confirmation
• Tab Put the focus on the next component or frame
• Shift+Tab Put the focus on the previous component or frame
• F6 Put the focus on the next frame
• Shift+F6 Put the focus on the previous frame
• Alt+F Show the File menu
• Alt+O Show the Options menu
• Alt+T Show the Tools menu
• Alt+H Show the Help menu
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Using GATE Developer
• F10 Show the first menu
Resources tree (Section 3.1):
• Enter Show the selected resources
• Ctrl+H Hide the selected resource
• Ctrl+Shift+H Hide all the resources
• F2 Rename the selected resource
• Ctrl+F4 Close the selected resource
Document editor (Section 3.2):
• Ctrl+F Show the search dialog for the document
• Ctrl+S Save the document in a file
• F3 Show/Hide the annotation sets
• Shift+F3 Show the annotation sets with preselection
• F4 Show/Hide the annotations list
• F5 Show/Hide the coreference editor
• F7 Show/Hide the text
Annotation editor (Section 3.4):
• Right/Left Grow/Shrink the annotation span at its start
• Alt+Right/Alt+Left Grow/Shrink the annotation span at its end
• +Shift/+Ctrl+Shift Use a span increment of 5/10 characters
• Alt+Delete Delete the currently edited annotation
Annic/Lucene datastore (Chapter 9):
• Alt+Enter Search the expression in the datastore
• Alt+Backspace Delete the search expression
Using GATE Developer
67
• Alt+Right Display the next page of results
• Alt+Left Display the row manager
• Alt+E Export the results to a file
Annic/Lucene query text field (Chapter 9):
• Ctrl+Enter Insert a new line
• Enter Search the expression
• Alt+Top Select the previous result
• Alt+Bottom Select the next result
3.10
Miscellaneous
3.10.1
Stopping GATE from Restoring Developer Sessions/Options
GATE can remember Developer options and the state of the resource tree when it exits. The
options are saved by default; the session state is not saved by default. This default behaviour
can be changed from the ‘Advanced’ tab of the ‘Configuration’ choice on the ‘Options’ menu.
If a problem occurs and the saved data prevents GATE Developer from starting, you can
fix this by deleting the configuration and session data files. These are stored in your home
directory, and are called gate.xml and gate.sesssion or .gate.xml and .gate.sesssion
depending on platform. On Windows your home is:
95, 98, NT: Windows Directory/profiles/username
2000, XP: Windows Drive/Documents and Settings/username
3.10.2
Working with Unicode
GATE provides various facilities for working with Unicode beyond those that come as default
with Java3 :
3
Implemented by Valentin Tablan, Mark Leisher and Markus Kramer. Initial version developed by Mark
Leisher.
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Using GATE Developer
1. a Unicode editor with input methods for many languages;
2. use of the input methods in all places where text is edited in the GUI;
3. a development kit for implementing input methods;
4. ability to read diverse character encodings.
1 using the editor:
In GATE Developer, select ‘Unicode editor’ from the ‘Tools’ menu. This will display an
editor window, and, when a language with a custom input method is selected for input (see
next section), a virtual keyboard window with the characters of the language assigned to the
keys on the keyboard. You can enter data either by typing as normal, or with mouse clicks
on the virtual keyboard.
2 configuring input methods:
In the editor and in GATE Developer’s main window, the ‘Options’ menu has an ‘Input
methods’ choice. All supported input languages (a superset of the JDK languages) are
available here. Note that you need to use a font capable of displaying the language you
select. By default GATE Developer will choose a Unicode font if it can find one on the
platform you’re running on. Otherwise, select a font manually from the ‘Options’ menu
‘Configuration’ choice.
3 using the development kit:
GUK, the GATE Unicode Kit, is documented at:
http://gate.ac.uk/gate/doc/javadoc/guk/package-summary.html.
4 reading different character encodings:
When you create a document from a URL pointing to textual data in GATE, you have to
tell the system what character encoding the text is stored in. By default, GATE will set this
parameter to be the empty string. This tells Java to use the default encoding for whatever
platform it is running on at the time – e.g. on Western versions of Windows this will be ISO8859-1, and Eastern ones ISO-8859-9. On Linux systems, the default encoding is influenced
by the LANG environment variable, e.g. when this variable is set to en US.utf-8 the default
encoding used will be UTF-8. When GATE is started using the bin/ant run command or
(on Linux) through the gate.sh script or a link to it, you can change the default encoding
used by GATE to UTF-8 by adding -Drun.file.encoding=utf-8 as a parameter.
A popular way to store Unicode documents is in UTF-8, which is a superset of ASCII (but
can still store all Unicode data); if you get an error message about document I/O during
reading, try setting the encoding to UTF-8, or some other locally popular encoding. (To see
a list of available encodings, try opening a document in GATE’s unicode editor – you will
be prompted to select an encoding.)
Chapter 4
CREOLE: the GATE Component
Model
. . . Noam Chomsky’s answer in Secrets, Lies and Democracy (David Barsamian
1994; Odonian) to ‘What do you think about the Internet?’
‘I think that there are good things about it, but there are also aspects of it that
concern and worry me. This is an intuitive response – I can’t prove it – but my
feeling is that, since people aren’t Martians or robots, direct face-to-face contact
is an extremely important part of human life. It helps develop self-understanding
and the growth of a healthy personality.
‘You just have a different relationship to somebody when you’re looking at them
than you do when you’re punching away at a keyboard and some symbols come
back. I suspect that extending that form of abstract and remote relationship,
instead of direct, personal contact, is going to have unpleasant effects on what
people are like. It will diminish their humanity, I think.’
Chomsky, quoted at http://philip.greenspun.com/wtr/dead-trees/53015.
The GATE architecture is based on components: reusable chunks of software with welldefined interfaces that may be deployed in a variety of contexts. The design of GATE is
based on an analysis of previous work on infrastructure for LE, and of the typical types
of software entities found in the fields of NLP and CL (see in particular chapters 4–6 of
[Cunningham 00]). Our research suggested that a profitable way to support LE software
development was an architecture that breaks down such programs into components of various
types. Because LE practice varies very widely (it is, after all, predominantly a research field),
the architecture must avoid restricting the sorts of components that developers can plug into
the infrastructure. The GATE framework accomplishes this via an adapted version of the
Java Beans component framework from Sun, as described in section 4.2.
GATE components may be implemented by a variety of programming languages and
databases, but in each case they are represented to the system as a Java class. This class
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CREOLE: the GATE Component Model
may do nothing other than call the underlying program, or provide an access layer to a
database; on the other hand it may implement the whole component.
GATE components are one of three types:
• LanguageResources (LRs) represent entities such as lexicons, corpora or ontologies;
• ProcessingResources (PRs) represent entities that are primarily algorithmic, such as
parsers, generators or ngram modellers;
• VisualResources (VRs) represent visualisation and editing components that participate
in GUIs.
The distinction between language resources and processing resources is explored more fully in
section C.1.1. Collectively, the set of resources integrated with GATE is known as CREOLE:
a Collection of REusable Objects for Language Engineering.
In the rest of this chapter:
• Section 4.3 describes the lifecycle of GATE components;
• Section 4.4 describes how Processing Resources can be grouped into applications;
• Section 4.5 describes the relationship between Language Resources and their datastores;
• Section 4.6 summarises GATE’s set of built-in components;
• Section 4.7 describes how configuration data for Resource types is supplied to GATE.
4.1
The Web and CREOLE
GATE allows resource implementations and Language Resource persistent data to be distributed over the Web, and uses Java annotations and XML for configuration of resources
(and GATE itself).
Resource implementations are grouped together as ‘plugins’, stored at a URL (when the
resources are in the local file system this can be a file:/ URL). When a plugin is loaded
into GATE it looks for a configuration file called creole.xml relative to the plugin URL and
uses the contents of this file to determine what resources this plugin declares and where to find
the classes that implement the resource types (typically these classes are stored in a JAR file
in the plugin directory). Configuration data for the resources may be stored directly in the
creole.xml file, or it may be stored as Java annotations on the resource classes themselves; in
either case GATE retrieves this configuration information and adds the resource definitions
CREOLE: the GATE Component Model
71
to the CREOLE register. When a user requests an instantiation of a resource, GATE creates
an instance of the resource class in the virtual machine.
Language resource data can be stored in binary serialised form in the local file system.
4.2
The GATE Framework
We can think of the GATE framework as a backplane into which users can plug CREOLE
components. The user gives the system a list of URLs to search when it starts up, and
components at those locations are loaded by the system.
The backplane performs these functions:
• component discovery, bootstrapping, loading and reloading;
• management and visualisation of native data structures for common information types;
• generalised data storage and process execution.
A set of components plus the framework is a deployment unit which can be embedded in
another application.
At their most basic, all GATE resources are Java Beans, the Java platform’s model of
software components. Beans are simply Java classes that obey certain interface conventions:
• beans must have no-argument constructors.
• beans have properties, defined by pairs of methods named by the convention setProp
and getProp .
GATE uses Java Beans conventions to construct and configure resources at runtime, and
defines interfaces that different component types must implement.
4.3
The Lifecycle of a CREOLE Resource
CREOLE resources exhibit a variety of forms depending on the perspective they are viewed
from. Their implementation is as a Java class plus an XML metadata file living at the
same URL. When using GATE Developer, resources can be loaded and viewed via the
resources tree (left pane) and the ‘create resource’ mechanism. When programming with
GATE Embedded, they are Java objects that are obtained by making calls to GATE’s
Factory class. These various incarnations are the phases of a CREOLE resource’s ‘lifecycle’.
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CREOLE: the GATE Component Model
Depending on what sort of task you are using GATE for, you may use resources in any or
all of these phases. For example, you may only be interested in getting a graphical view of
what GATE’s ANNIE Information Extraction system (see Chapter 6) does; in this case you
will use GATE Developer to load the ANNIE resources, and load a document, and create
an ANNIE application and run it on the document. If, on the other hand, you want to
create your own resources, or modify the Java code of an existing resource (as opposed to
just modifying its grammar, for example), you will need to deal with all the lifecycle phases.
The various phases may be summarised as:
Creating a new resource from scratch (bootstrapping). To create the binary image
of a resource (a Java class in a JAR file), and the XML file that describes the resource
to GATE, you need to create the appropriate .java file(s), compile them and package
them as a .jar. GATE provides a bootstrap tool to start this process – see Section
7.11. Alternatively you can simply copy code from an existing resource.
Instantiating a resource in GATE Embedded. To create a resource in your own Java
code, use GATE’s Factory class (this takes care of parameterising the resource, restoring it from a database where appropriate, etc. etc.). Section 7.2 describes how to do
this.
Loading a resource into GATE Developer. To load a resource into GATE Developer,
use the various ‘New ... resource’ options from the File menu and elsewhere. See
Section 3.1.
Resource configuration and implementation. GATE’s bootstrap tool will create an
empty resource that does nothing. In order to achieve the behaviour you require,
you’ll need to change the configuration of the resource (by editing the creole.xml
file) and/or change the Java code that implements the resource. See section 4.7.
4.4
Processing Resources and Applications
PRs can be combined into applications. Applications model a control strategy for the execution of PRs. In GATE, applications are called ‘controllers’ accordingly.
Currently only sequential, or pipeline, execution is supported. There are two main types of
pipeline:
Simple pipelines simply group a set of PRs together in order and execute them in turn.
The implementing class is called SerialController.
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73
Corpus pipelines are specific for LanguageAnalysers – PRs that are applied to documents
and corpora. A corpus pipeline opens each document in the corpus in turn, sets that
document as a runtime parameter on each PR, runs all the PRs on the corpus, then
closes the document. The implementing class is called SerialAnalyserController.
Conditional versions of these controllers are also available. These allow processing resources
to be run conditionally on document features. See Section 3.7.2 for how to use these. If more
flexibility is required, the Groovy plugin provides a scriptable controller (see section 7.16.3)
whose execution strategy is specified using the Groovy programming language.
Controllers are themselves PRs – in particular a simple pipeline is a standard PR and a
corpus pipeline is a LanguageAnalyser – so one pipeline can be nested in another. This is
particularly useful with conditional controllers to group together a set of PRs that can all
be turned on or off as a group.
There is also a real-time version of the corpus pipeline. When creating such a controller,
a timeout parameter needs to be set which determines the maximum amount of time (in
milliseconds) allowed for the processing of a document. Documents that take longer to
process, are simply ignored and the execution moves to the next document after the timeout
interval has lapsed.
All controllers have special handling for processing resources that implement the interface
gate.creole.ControllerAwarePR. This interface provides methods that are called by the
controller at the start and end of the whole application’s execution – for a corpus pipeline,
this means before any document has been processed and after all documents in the corpus
have been processed, which is useful for PRs that need to share data structures across the
whole corpus, build aggregate statistics, etc. For full details, see the JavaDoc documentation
for ControllerAwarePR.
4.5
Language Resources and Datastores
Language Resources can be stored in Datastores. Datastores are an abstract model of diskbased persistence, which can be implemented by various types of storage mechanism. Here
are the types implemented:
Serial Datastores are based on Java’s serialisation system, and store data directly into
files and directories.
Lucene Datastores is a full-featured annotation indexing and retrieval system. It is provided as part of an extension of the Serial Datastores. See Section 9 for more details.
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CREOLE: the GATE Component Model
4.6
Built-in CREOLE Resources
GATE comes with various built-in components:
• Language Resources modelling Documents and Corpora, and various types of Annotation Schema – see Chapter 5.
• Processing Resources that are part of the ANNIE system – see Chapter 6.
• Gazetteers – see Chapter 13.
• Ontologies – see Chapter 14.
• Machine Learning resources – see Chapter 17.
• Alignment tools – see Chapter 18.
• Parsers and taggers – see Chapter 16.
• Other miscellaneous resources – see Chapter 20.
4.7
CREOLE Resource Configuration
This section describes how to supply GATE with the configuration data it needs about a
resource, such as what its parameters are, how to display it if it has a visualisation, etc.
Several GATE resources can be grouped into a single plugin, which is a directory containing
an XML configuration file called creole.xml. Configuration data for the plugin’s resources
can be given in the creole.xml file or directly in the Java source file using Java annotations.
A creole.xml file has a root element <CREOLE-DIRECTORY>, but the further contents of this
element depend on the configuration style. The following three sections discuss the different
styles – all-XML, all-annotations and a mixture of the two.
4.7.1
Configuration with XML
To configure your resources in the creole.xml file, the <CREOLE-DIRECTORY> element should
contain one <RESOURCE> element for each resource type in the plugin. The <RESOURCE> elements may optionally be contained within a <CREOLE> element (to allow a single creole.xml
file to be built up by concatenating multiple separate files). For example:
CREOLE: the GATE Component Model
<CREOLE-DIRECTORY>
<CREOLE>
<RESOURCE>
<NAME>Minipar Wrapper</NAME>
<JAR>MiniparWrapper.jar</JAR>
<CLASS>minipar.Minipar</CLASS>
<COMMENT>MiniPar is a shallow parser. It determines the
dependency relationships between the words of a sentence.</COMMENT>
<HELPURL>http://gate.ac.uk/cgi-bin/userguide/sec:parsers:minipar</HELPURL>
<PARAMETER NAME="document"
RUNTIME="true"
COMMENT="document to process">gate.Document</PARAMETER>
<PARAMETER NAME="miniparDataDir"
RUNTIME="true"
COMMENT="location of the Minipar data directory">
java.net.URL
</PARAMETER>
<PARAMETER NAME="miniparBinary"
RUNTIME="true"
COMMENT="Name of the Minipar command file">
java.net.URL
</PARAMETER>
<PARAMETER NAME="annotationInputSetName"
RUNTIME="true"
OPTIONAL="true"
COMMENT="Name of the input Source">
java.lang.String
</PARAMETER>
<PARAMETER NAME="annotationOutputSetName"
RUNTIME="true"
OPTIONAL="true"
COMMENT="Name of the output AnnotationSetName">
java.lang.String
</PARAMETER>
<PARAMETER NAME="annotationTypeName"
RUNTIME="false"
DEFAULT="DepTreeNode"
COMMENT="Annotations to store with this type">
java.lang.String
</PARAMETER>
</RESOURCE>
</CREOLE>
</CREOLE-DIRECTORY>
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CREOLE: the GATE Component Model
Basic Resource-Level Data
Each resource must give a name, a Java class and the JAR file that it can be loaded from.
The above example is taken from the Parser Minipar plugin, and defines a single resource
with a number of parameters.
The full list of valid elements under <RESOURCE> is as follows:
NAME the name of the resource, as it will appear in the ‘New’ menu in GATE Developer.
If omitted, defaults to the bare name of the resource class (without a package name).
CLASS the fully qualified name of the Java class that implements this resource.
JAR names JAR files required by this resource (paths are relative to the location of
creole.xml). Typically this will be the JAR file containing the class named by the
<CLASS> element, but additional <JAR> elements can be used to name third-party JAR
files that the resource depends on.
COMMENT a descriptive comment about the resource, which will appear as the tooltip
when hovering over an instance of this resource in the resources tree in GATE Developer. If omitted, no comment is used.
HELPURL a URL to a help document on the web for this resource. It is used in the help
browser inside GATE Developer.
INTERFACE the interface type implemented by this resource, for example new types of
document would specify <INTERFACE>gate.Document</INTERFACE>.
ICON the icon used to represent this resource in GATE Developer. This is a path inside
the plugin’s JAR file, for example <ICON>/some/package/icon.png</ICON>. If the
path specified does not start with a forward slash, it is assumed to name an icon from
the GATE default set, which is located in gate.jar at gate/resources/img. If no icon
is specified, a generic language resource or processing resource icon (as appropriate) is
used.
PRIVATE if present, this resource type is hidden in the GATE Developer GUI, i.e. it is
not shown in the ‘New’ menus. This is useful for resource types that are intended to be
created internally by other resources, or for resources that have parameters of a type
that cannot be set in the GUI. <PRIVATE/> resources can still be created in Java code
using the Factory.
AUTOINSTANCE (and HIDDEN-AUTOINSTANCE) tells GATE to automatically create instances of this resource when the plugin is loaded. Any number of auto
instances may be defined, GATE will create them all. Each <AUTOINSTANCE> element
may optionally contain <PARAM NAME="..." VALUE="..." /> elements giving parameter values to use when creating the instance. Any parameters not specified explicitly
CREOLE: the GATE Component Model
77
will take their default values. Use <HIDDEN-AUTOINSTANCE> if you want the auto instances not to show up in GATE Developer – this is useful for things like document
formats where there should only ever be a single instance in GATE and that instance
should not be deleted.
TOOL if present, this resource type is considered to be a “tool”. Tools can contribute items
to the Tools menu in GATE Developer.
For visual resources, a <GUI> element should also be provided. This takes a TYPE attribute,
which can have the value LARGE or SMALL. LARGE means that the visual resource is a large
viewer and should appear in the main part of the GATE Developer window on the right
hand side, SMALL means the VR is a small viewer which appears in the space below the
resources tree in the bottom left. The <GUI> element supports the following sub-elements:
RESOURCE DISPLAYED the type of GATE resource this VR can display. Any resource whose type is assignable to this type will be displayed with this viewer, so for
example a VR that can display all types of document would specify gate.Document,
whereas a VR that can only display the default GATE document implementation would
specify gate.corpora.DocumentImpl.
MAIN VIEWER if present, GATE will consider this VR to be the ‘most important’
viewer for the given resource type, and will ensure that if several different viewers are
all applicable to this resource, this viewer will be the one that is initially visible.
For annotation viewers, you should specify an <ANNOTATION_TYPE_DISPLAYED> element giving the annotation type that the viewer can display (e.g. Sentence).
Resource Parameters
Resources may also have parameters of various types. These resources, from the GATE
distribution, illustrate the various types of parameters:
<RESOURCE>
<NAME>GATE document</NAME>
<CLASS>gate.corpora.DocumentImpl</CLASS>
<INTERFACE>gate.Document</INTERFACE>
<COMMENT>GATE transient document</COMMENT>
<OR>
<PARAMETER NAME="sourceUrl"
SUFFIXES="txt;text;xml;xhtm;xhtml;html;htm;sgml;sgm;mail;email;eml;rtf"
COMMENT="Source URL">java.net.URL</PARAMETER>
<PARAMETER NAME="stringContent"
COMMENT="The content of the document">java.lang.String</PARAMETER>
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CREOLE: the GATE Component Model
</OR>
<PARAMETER
COMMENT="Should the document read the original markup"
NAME="markupAware" DEFAULT="true">java.lang.Boolean</PARAMETER>
<PARAMETER NAME="encoding" OPTIONAL="true"
COMMENT="Encoding" DEFAULT="">java.lang.String</PARAMETER>
<PARAMETER NAME="sourceUrlStartOffset"
COMMENT="Start offset for documents based on ranges"
OPTIONAL="true">java.lang.Long</PARAMETER>
<PARAMETER NAME="sourceUrlEndOffset"
COMMENT="End offset for documents based on ranges"
OPTIONAL="true">java.lang.Long</PARAMETER>
<PARAMETER NAME="preserveOriginalContent"
COMMENT="Should the document preserve the original content"
DEFAULT="false">java.lang.Boolean</PARAMETER>
<PARAMETER NAME="collectRepositioningInfo"
COMMENT="Should the document collect repositioning information"
DEFAULT="false">java.lang.Boolean</PARAMETER>
<ICON>lr.gif</ICON>
</RESOURCE>
<RESOURCE>
<NAME>Document Reset PR</NAME>
<CLASS>gate.creole.annotdelete.AnnotationDeletePR</CLASS>
<COMMENT>Document cleaner</COMMENT>
<PARAMETER NAME="document" RUNTIME="true">gate.Document</PARAMETER>
<PARAMETER NAME="annotationTypes" RUNTIME="true"
OPTIONAL="true">java.util.ArrayList</PARAMETER>
</RESOURCE>
Parameters may be optional, and may have default values (and may have comments to
describe their purpose, which is displayed by GATE Developer during interactive parameter
setting).
Some PR parameters are execution time (RUNTIME), some are initialisation time. E.g. at
execution time a doc is supplied to a language analyser; at initialisation time a grammar
may be supplied to a language analyser.
The <PARAMETER> tag takes the following attributes:
NAME: name of the JavaBean property that the parameter refers to, i.e. for a parameter
named ‘someParam’ the class must have setSomeParam and getSomeParam methods.1
1
The JavaBeans spec allows is instead of get for properties of the primitive type boolean, but GATE
does not support parameters with primitive types. Parameters of type java.lang.Boolean (the wrapper
class) are permitted, but these have get accessors anyway.
CREOLE: the GATE Component Model
79
DEFAULT: default value (see below).
RUNTIME: doesn’t need setting at initialisation time, but must be set before calling
execute(). Only meaningful for PRs
OPTIONAL: not required
COMMENT: for display purposes
ITEM CLASS NAME: (only applies to parameters whose type is java.util.Collection
or a type that implements or extends this) this specifies the type of elements the collection contains, so GATE can use the right type when parameters are set. If omitted,
GATE will pass in the elements as Strings.
SUFFIXES: (only applies to parameters of type java.net.URL) a semicolon-separated list
of file suffixes that this parameter typically accepts, used as a filter in the file chooser
provided by GATE Developer to select a local file as the parameter value.
It is possible for two or more parameters to be mutually exclusive (i.e. a user must specify
one or the other but not both). In this case the <PARAMETER> elements should be grouped
together under an <OR> element.
The type of the parameter is specified as the text of the <PARAMETER> element, and the type
supplied must match the return type of the parameter’s get method. Any reference type
(class, interface or enum) may be used as the parameter type, including other resource types –
in this case GATE Developer will offer a list of the loaded instances of that resource as options
for the parameter value. Primitive types (char, boolean, . . . ) are not supported, instead you
should use the corresponding wrapper type (java.lang.Character, java.lang.Boolean,
. . . ). If the getter returns a parameterized type (e.g. List<Integer>) you should just specify
the raw type (java.util.List) here2 .
The DEFAULT string is converted to the appropriate type for the parameter java.lang.String parameters use the value directly, primitive wrapper types e.g.
java.lang.Integer use their respective valueOf methods, and other built-in Java types
can have defaults specified provided they have a constructor taking a String.
The type java.net.URL is treated specially: if the default string is not an absolute URL (e.g.
http://gate.ac.uk/) then it is treated as a path relative to the location of the creole.xml file.
Thus a DEFAULT of ‘resources/main.jape’ in the file file:/opt/MyPlugin/creole.xml
is treated as the absolute URL file:/opt/MyPlugin/resources/main.jape.
For Collection-valued parameters multiple values may be specified, separated by semicolons, e.g. ‘foo;bar;baz’; if the parameter’s type is an interface – Collection or one of
its sub-interfaces (e.g. List) – a suitable concrete class (e.g. ArrayList, HashSet) will be
chosen automatically for the default value.
2
In this particular case, as the type is a collection, you would specify java.lang.Integer as the
ITEM CLASS NAME.
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CREOLE: the GATE Component Model
For parameters of type gate.FeatureMap multiple name=value pairs can be specified, e.g.
‘kind=word;orth=upperInitial’. For enum-valued parameters the default string is taken
as the name of the enum constant to use. Finally, if no DEFAULT attribute is specified, the
default value is null.
4.7.2
Configuring Resources using Annotations
As an alternative to the XML configuration style, GATE provides Java annotation types
to embed the configuration data directly in the Java source code. @CreoleResource is
used to mark a class as a GATE resource, and parameter information is provided through
annotations on the JavaBean set methods. At runtime these annotations are read and
mapped into the equivalent entries in creole.xml before parsing. The metadata annotation
types are all marked @Documented so the CREOLE configuration data will be visible in the
generated JavaDoc documentation.
For more detailed information, see the JavaDoc documentation for gate.creole.metadata.
To use annotation-driven configuration for a plugin a creole.xml file is still required but it
need only contain the following:
<CREOLE-DIRECTORY>
<JAR SCAN="true">myPlugin.jar</JAR>
<JAR>lib/thirdPartyLib.jar</JAR>
</CREOLE-DIRECTORY>
This tells GATE to load myPlugin.jar and scan its contents looking for resource classes
annotated with @CreoleResource. Other JAR files required by the plugin can be specified
using other <JAR> elements without SCAN="true".
In a GATE Embedded application it is possible to register a single @CreoleResource annotated class without using a creole.xml file by calling
Gate.getCreoleRegister().registerComponent(MyResource.class);
GATE will extract the configuration from the annotations on the class and make it available
for use as if it had been defined in a plugin.
Basic Resource-Level Data
To mark a class as a CREOLE resource, simply use the @CreoleResource annotation (in
the gate.creole.metadata package), for example:
CREOLE: the GATE Component Model
1
2
81
import gate . creole . A b s t r a c t L a n g u a g e A n a l y s e r ;
import gate . creole . metadata .*;
3
4
5
6
7
@CreoleResource ( name = " GATE Tokeniser " ,
comment = " Splits text into tokens and spaces " )
public class Tokeniser extends A b s t r a c t L a n g u a g e A n a l y s e r {
...
The @CreoleResource annotation provides slots for all the values that can be specified under
<RESOURCE> in creole.xml, except <CLASS> (inferred from the name of the annotated class)
and <JAR> (taken to be the JAR containing the class):
name (String) the name of the resource, as it will appear in the ‘New’ menu in GATE
Developer. If omitted, defaults to the bare name of the resource class (without a
package name). (XML equivalent <NAME>)
comment (String) a descriptive comment about the resource, which will appear as the
tooltip when hovering over an instance of this resource in the resources tree in GATE
Developer. If omitted, no comment is used. (XML equivalent <COMMENT>)
helpURL (String) a URL to a help document on the web for this resource. It is used in
the help browser inside GATE Developer. (XML equivalent <HELPURL>)
isPrivate (boolean) should this resource type be hidden from the GATE Developer GUI, so
it does not appear in the ‘New’ menus? If omitted, defaults to false (i.e. not hidden).
(XML equivalent <PRIVATE/>)
icon (String) the icon to use to represent the resource in GATE Developer. If omitted, a
generic language resource or processing resource icon is used. (XML equivalent <ICON>,
see the description above for details)
interfaceName (String) the interface type implemented by this resource, for example
a new type of document would specify "gate.Document" here. (XML equivalent
<INTERFACE>)
autoInstances (array of @AutoInstance annotations) definitions for any instances of this
resource that should be created automatically when the plugin is loaded. If omitted, no
auto-instances are created by default. (XML equivalent, one or more <AUTOINSTANCE>
and/or <HIDDEN-AUTOINSTANCE> elements, see the description above for details)
tool (boolean) is this resource type a tool?
For visual resources only, the following elements are also available:
guiType (GuiType enum) the type of GUI this resource defines.
<GUI TYPE="LARGE|SMALL">)
(XML equivalent
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CREOLE: the GATE Component Model
resourceDisplayed (String) the class name of the resource type that this VR displays, e.g.
"gate.Corpus". (XML equivalent <RESOURCE_DISPLAYED>)
mainViewer (boolean) is this VR the ‘most important’ viewer for its displayed resource
type? (XML equivalent <MAIN_VIEWER/>, see above for details)
For annotation viewers, you should specify an annotationTypeDisplayed element giving
the annotation type that the viewer can display (e.g. Sentence).
Resource Parameters
Parameters are declared by placing annotations on their JavaBean set methods. To mark
a setter method as a parameter, use the @CreoleParameter annotation, for example:
@CreoleParameter(comment = "The location of the list of abbreviations")
public void setAbbrListUrl(URL listUrl) {
...
GATE will infer the parameter’s name from the name of the JavaBean property in the usual
way (i.e. strip off the leading set and convert the following character to lower case, so in
this example the name is abbrListUrl). The parameter name is not taken from the name
of the method parameter. The parameter’s type is inferred from the type of the method
parameter (java.net.URL in this case).
The annotation elements of @CreoleParameter correspond to the attributes of the
<PARAMETER> tag in the XML configuration style:
comment (String) an optional descriptive comment about the parameter. (XML equivalent
COMMENT)
defaultValue (String) the optional default value for this parameter. The value is specified
as a string but is converted to the relevant type by GATE according to the conversions
described in the previous section. Note that relative path default values for URL-valued
parameters are still relative to the location of the creole.xml file, not the annotated
class3 . (XML equivalent DEFAULT)
suffixes (String) for URL-valued parameters, a semicolon-separated list of default file suffixes that this parameter accepts. (XML equivalent SUFFIXES)
3
When registering a class using CreoleRegister.registerComponent the base URL against which defaults for URL parameters are resolved is not specified. In such a resource it may be better to use
Class.getResource to construct the default URLs if no value is supplied for the parameter by the user.
CREOLE: the GATE Component Model
83
collectionElementType (Class) for Collection-valued parameters, the type of the elements in the collection. This can usually be inferred from the generic type information, for example public void setIndices(List<Integer> indices), but must be
specified if the set method’s parameter has a raw (non-parameterized) type. (XML
equivalent ITEM_CLASS_NAME)
Mutually-exclusive parameters (such as would be grouped in an <OR> in creole.xml) are
handled by adding a disjunction="label" and priority=n to the @CreoleParameter annotation – all parameters that share the same label are grouped in the same disjunction,
and will be offered in order of priority. The parameter with the smallest priority value will
be the one listed first, and thus the one that is offered initially when creating a resource
of this type in GATE Developer. For example, the following is a simplified extract from
gate.corpora.DocumentImpl:
1
2
@CreoleParameter ( disjunction = " src " , priority =1)
public void setSourceUrl ( URL src ) { /∗ ∗/ }
3
4
5
@CreoleParameter ( disjunction = " src " , priority =2)
public void setStringContent ( String content ) { /∗ ∗/ }
This declares the parameters “stringContent” and “sourceUrl” as mutually-exclusive, and
when creating an instance of this resource in GATE Developer the parameter that will be
shown initially is sourceUrl. To set stringContent instead the user must select it from the
drop-down list. Parameters with the same declared priority value will appear next to each
other in the list, but their relative ordering is not specified. Parameters with no explicit
priority are always listed after those that do specify a priority.
Optional and runtime parameters are marked using extra annotations, for example:
1
2
3
4
5
@Optional
@RunTime
@CreoleParameter
public void s e t A n n o t a t i o n S e t N a m e ( String asName ) {
...
Inheritance
Unlike with pure XML configuration, when using annotations a resource will inherit any
configuration data that was not explicitly specified from annotations on its parent class
and on any interfaces it implements. Specifically, if you do not specify a comment, interfaceName, icon, annotationTypeDisplayed or the GUI-related elements (guiType and resourceDisplayed) on your @CreoleResource annotation then GATE will look up the class
tree for other @CreoleResource annotations, first on the superclass, its superclass, etc.,
then at any implemented interfaces, and use the first value it finds. This is useful if you are
defining a family of related resources that inherit from a common base class.
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CREOLE: the GATE Component Model
The resource name and the isPrivate and mainViewer flags are not inherited.
Parameter definitions are inherited in a similar way. This is one of the big advantages of
annotation configuration over pure XML – if one resource class extends another then with
pure XML configuration all the parent class’s parameter definitions must be duplicated in
the subclass’s creole.xml definition. With annotations, parameters are inherited from the
parent class (and its parent, etc.) as well as from any interfaces implemented. For example, the gate.LanguageAnalyser interface provides two parameter definitions via annotated
set methods, for the corpus and document parameters. Any @CreoleResource annotated
class that implements LanguageAnalyser, directly or indirectly, will get these parameters
automatically.
Of course, there are some cases where this behaviour is not desirable, for example if a subclass
calculates a value for a superclass parameter rather than having the user set it directly. In
this case you can hide the parameter by overriding the set method in the subclass and using
a marker annotation:
1
2
3
4
@HiddenCreoleParameter
public void setSomeParam ( String someParam ) {
super . setSomeParam ( someParam );
}
The overriding method will typically just call the superclass one, as its only purpose is to
provide a place to put the @HiddenCreoleParameter annotation.
Alternatively, you may want to override some of the configuration for a parameter but inherit
the rest from the superclass. Again, this is handled by trivially overriding the set method
and re-annotating it:
1
2
3
4
5
6
// superclass
@CreoleParameter ( comment = " Location of the grammar file " ,
suffixes = " jape " )
public void setGrammarUrl ( URL grammarLocation ) {
...
}
7
8
9
10
11
12
13
1
2
@Optional
@RunTime
@CreoleParameter ( comment = " Feature to set on success " )
public void se tSucce ssFeat ure ( String name ) {
...
}
//———————————–
// subclass
3
4
5
6
7
8
// override the default value, inherit everything else
@CreoleParameter ( defaultValue = " resources / defaultGrammar . jape " )
public void setGrammarUrl ( URL url ) {
super . setGrammarUrl ( url );
}
CREOLE: the GATE Component Model
85
9
10
11
12
13
14
15
// we want the parameter to be required in the subclass
@Optional ( false )
@CreoleParameter
public void se tSucce ssFeat ure ( String name ) {
super . setSu ccessF eature ( name );
}
Note that for backwards compatibility, data is only inherited from superclass annotations
if the subclass is itself annotated with @CreoleResource. If the subclass is not annotated
then GATE assumes that all its configuration is contained in creole.xml in the usual way.
4.7.3
Mixing the Configuration Styles
It is possible and often useful to mix and match the XML and annotation-driven configuration styles. The rule is always that anything specified in the XML takes priority over the
annotations. The following examples show what this allows.
Overriding Configuration for a Third-Party Resource
Suppose you have a plugin from some third party that uses annotation-driven configuration.
You don’t have the source code but you would like to override the default value for one of
the parameters of one of the plugin’s resources. You can do this in the creole.xml:
<CREOLE-DIRECTORY>
<JAR SCAN="true">acmePlugin-1.0.jar</JAR>
<!-- Add the following to override the annotations -->
<RESOURCE>
<CLASS>com.acme.plugin.UsefulPR</CLASS>
<PARAMETER NAME="listUrl"
DEFAULT="resources/myList.txt">java.net.URL</PARAMETER>
</RESOURCE>
</CREOLE-DIRECTORY>
The default value for the listUrl parameter in the annotated class will be replaced by your
value.
External AUTOINSTANCEs
For resources like document formats, where there should always and only be one instance in GATE at any time, it makes sense to put the auto-instance definitions in the
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CREOLE: the GATE Component Model
@CreoleResource annotation. But if the automatically created instances are a convenience
rather than a necessity it may be better to define them in XML so other users can disable
them without re-compiling the class:
<CREOLE-DIRECTORY>
<JAR SCAN="true">myPlugin.jar</JAR>
<RESOURCE>
<CLASS>com.acme.AutoPR</CLASS>
<AUTOINSTANCE>
<PARAM NAME="type" VALUE="Sentence" />
</AUTOINSTANCE>
<AUTOINSTANCE>
<PARAM NAME="type" VALUE="Paragraph" />
</AUTOINSTANCE>
</RESOURCE>
</CREOLE-DIRECTORY>
Inheriting Parameters
If you would prefer to use XML configuration for your own resources, but would like to benefit
from the parameter inheritance features of the annotation-driven approach, you can write a
normal creole.xml file with all your configuration and just add a blank @CreoleResource
annotation to your class. For example:
1
2
3
package com . acme ;
import gate .*;
import gate . creole . metadata . CreoleResource ;
4
5
6
7
8
@CreoleResource
public class MyPR implements LanguageAnalyser {
...
}
<!-- creole.xml -->
<CREOLE-DIRECTORY>
<CREOLE>
<RESOURCE>
<NAME>My Processing Resource</NAME>
<CLASS>com.acme.MyPR</CLASS>
<COMMENT>...</COMMENT>
<PARAMETER NAME="annotationSetName"
RUNTIME="true" OPTIONAL="true">java.lang.String</PARAMETER>
<!-don’t need to declare document and corpus parameters, they
are inherited from LanguageAnalyser
CREOLE: the GATE Component Model
87
-->
</RESOURCE>
</CREOLE>
</CREOLE-DIRECTORY>
N.B. Without the @CreoleResource the parameters would not be inherited.
4.8
Tools: How to Add Utilities to GATE Developer
Visual Resources allow a developer to provide a GUI to interact with a particular resource
type (PR or LR), but sometimes it is useful to provide general utilities for use in the GATE
Developer GUI that are not tied to any specific resource type. Examples include the annotation diff tool and the Groovy console (provided by the Groovy plugin), both of which
are self-contained tools that display in their own top-level window. To support this, the
CREOLE model has the concept of a tool.
A resource type is marked as a tool by using the <TOOL/> element in its creole.xml
definition, or by setting tool = true if using the @CreoleResource annotation configuration style. If a resource is declared to be a tool, and written to implement the
gate.gui.ActionsPublisher interface, then whenever an instance of the resource is created
its published actions will be added to the “Tools” menu in GATE Developer.
Since the published actions of every instance of the resource will be added to the tools menu,
it is best not to use this mechanism on resource types that can be instantiated by the user.
The “tool” marker is best used in combination with the “private” flag (to hide the resource
from the list of available types in the GUI) and one or more hidden autoinstance definitions
to create a limited number of instances of the resource when its defining plugin is loaded.
See the GroovySupport resource in the Groovy plugin for an example of this.
4.8.1
Putting your tools in a sub-menu
If your plugin provides a number of tools (or a number of actions from the same tool) you
may wish to organise your actions into one or more sub-menus, rather than placing them
all on the single top-level tools menu. To do this, you need to put a special value into the
actions returned by the tool’s getActions() method:
1
2
action . putValue ( GateConstants . MENU_PATH_KEY ,
new String [] { " Acme toolkit " , " Statistics " });
The key must be GateConstants.MENU_PATH_KEY and the value must be an array of strings. Each
string in the array represents the name of one level of sub-menus. Thus in the example above
the action would be placed under “Tools → Acme toolkit → Statistics”. If no MENU_PATH_KEY
value is provided the action will be placed directly on the Tools menu.
88
CREOLE: the GATE Component Model
Chapter 5
Language Resources: Corpora,
Documents and Annotations
Sometimes in life you’ve got to dance like nobody’s watching.
...
I think they should introduce ‘sleeping’ to the Olympics. It would be an excellent
field event, in which the ‘athletes’ (for want of a better word) all lay down in
beds, just beyond where the javelins land, and the first one to fall asleep and
not wake up for three hours would win gold. I, for one, would be interested
in seeing what kind of personality would be suited to sleeping in a competitive
environment.
...
Life is a mystery to be lived, not a problem to be solved.
Round Ireland with a Fridge, Tony Hawks, 1998 (pp. 119, 147, 179).
This chapter documents GATE’s model of corpora, documents and annotations on documents. Section 5.1 describes the simple attribute/value data model that corpora, documents
and annotations all share. Section 5.2, Section 5.3 and Section 5.4 describe corpora, documents and annotations on documents respectively. Section 5.5 describes GATE’s support
for diverse document formats, and Section 5.5.2 describes facilities for XML input/output.
5.1
Features: Simple Attribute/Value Data
GATE has a single model for information that describes documents, collections of documents
(corpora), and annotations on documents, based on attribute/value pairs. Attribute names
are strings; values can be any Java object. The API for accessing this feature data is Java’s
Map interface (part of the Collections API).
89
90
5.2
Language Resources: Corpora, Documents and Annotations
Corpora: Sets of Documents plus Features
A Corpus in GATE is a Java Set whose members are Documents. Both Corpora and Documents are types of LanguageResource (LR); all LRs have a FeatureMap (a Java Map) associated with them that stored attribute/value information about the resource. FeatureMaps
are also used to associate arbitrary information with ranges of documents (e.g. pieces of
text) via the annotation model (see below).
Documents have a DocumentContent which is a text at present (future versions may add
support for audiovisual content) and one or more AnnotationSets which are Java Sets.
5.3
Documents: Content plus Annotations plus Features
Documents are modelled as content plus annotations (see Section 5.4) plus features (see
Section 5.1). The content of a document can be any subclass of DocumentContent.
5.4
Annotations: Directed Acyclic Graphs
Annotations are organised in graphs, which are modelled as Java sets of Annotation. Annotations may be considered as the arcs in the graph; they have a start Node and an end
Node, an ID, a type and a FeatureMap. Nodes have pointers into the sources document, e.g.
character offsets.
5.4.1
Annotation Schemas
Annotation schemas provide a means to define types of annotations in GATE.
GATE uses the XML Schema language supported by W3C for these definitions.
When using GATE Developer to create/edit annotations, a component is available
(gate.gui.SchemaAnnotationEditor) which is driven by an annotation schema file. This
component will constrain the data entry process to ensure that only annotations that correspond to a particular schema are created. (Another component allows unrestricted annotations to be created.)
Schemas are resources just like other GATE components. Below we give some examples of
such schemas. Section 3.4.6 describes how to create new schemas.
Language Resources: Corpora, Documents and Annotations
Date Schema
<?xml version="1.0"?>
<schema
xmlns="http://www.w3.org/2000/10/XMLSchema">
<!-- XSchema deffinition for Date-->
<element name="Date">
<complexType>
<attribute name="kind" use="optional">
<simpleType>
<restriction base="string">
<enumeration value="date"/>
<enumeration value="time"/>
<enumeration value="dateTime"/>
</restriction>
</simpleType>
</attribute>
</complexType>
</element>
</schema>
Person Schema
<?xml version="1.0"?>
<schema
xmlns="http://www.w3.org/2000/10/XMLSchema">
<!-- XSchema definition for Person-->
<element name="Person" />
</schema>
Address Schema
<?xml version="1.0"?> <schema
xmlns="http://www.w3.org/2000/10/XMLSchema">
<!-- XSchema deffinition for Address-->
<element name="Address">
<complexType>
<attribute name="kind" use="optional">
<simpleType>
<restriction base="string">
<enumeration value="email"/>
<enumeration value="url"/>
<enumeration value="phone"/>
<enumeration value="ip"/>
91
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Language Resources: Corpora, Documents and Annotations
<enumeration
<enumeration
<enumeration
<enumeration
</restriction>
</simpleType>
</attribute>
</complexType>
</element>
</schema>
5.4.2
value="street"/>
value="postcode"/>
value="country"/>
value="complete"/>
Examples of Annotated Documents
This section shows some simple examples of annotated documents.
This material is adapted from [Grishman 97], the TIPSTER Architecture Design document
upon which GATE version 1 was based. Version 2 has a similar model, although annotations
are now graphs, and instead of multiple spans per annotation each annotation now has a single start/end node pair. The current model is largely compatible with [Bird & Liberman 99],
and roughly isomorphic with "stand-off markup" as latterly adopted by the SGML/XML
community.
Each example is shown in the form of a table. At the top of the table is the document being
annotated; immediately below the line with the document is a ruler showing the position
(byte offset) of each character (see TIPSTER Architecture Design Document).
Underneath this appear the annotations, one annotation per line. For each annotation is
shown its Id, Type, Span (start/end offsets derived from the start/end nodes), and Features.
Integers are used as the annotation Ids. The features are shown in the form name = value.
The first example shows a single sentence and the result of three annotation procedures: tokenization with part-of-speech assignment, name recognition, and sentence boundary recognition. Each token has a single feature, its part of speech (pos), using the tag set from the
University of Pennsylvania Tree Bank; each name also has a single feature, indicating the
type of name: person, company, etc.
Annotations will typically be organized to describe a hierarchical decomposition of a text.
A simple illustration would be the decomposition of a sentence into tokens. A more complex
case would be a full syntactic analysis, in which a sentence is decomposed into a noun phrase
and a verb phrase, a verb phrase into a verb and its complement, etc. down to the level of
individual tokens. Such decompositions can be represented by annotations on nested sets
of spans. Both of these are illustrated in the second example, which is an elaboration of
our first example to include parse information. Each non-terminal node in the parse tree is
represented by an annotation of type parse.
Language Resources: Corpora, Documents and Annotations
Id
1
2
3
4
5
6
7
Text
Cyndi savored the soup.
^0...^5...^10..^15..^20
Annotations
Type
SpanStart Span End Features
token
0
5
pos=NP
token
6
13
pos=VBD
token
14
17
pos=DT
token
18
22
pos=NN
token
22
23
name
0
5
name type=person
sentence 0
23
Table 5.1: Result of annotation on a single sentence
Text
Cyndi savored the soup.
^0...^5...^10..^15..^20
Annotations
Id Type
SpanStart Span End Features
1 token
0
5
pos=NP
2 token
6
13
pos=VBD
3 token
14
17
pos=DT
4 token
18
22
pos=NN
5 token
22
23
6 name
0
5
name type=person
7 sentence 0
23
constituents=[1],[2],[3].[4],[5]
Table 5.2: Result of annotations including parse information
93
94
Language Resources: Corpora, Documents and Annotations
Text
To: All Barnyard Animals
^0...^5...^10..^15..^20.
From: Chicken Little
^25..^30..^35..^40..
Date: November 10,1194
...^50..^55..^60..^65.
Subject: Descending Firmament
.^70..^75..^80..^85..^90..^95
Priority: Urgent
.^100.^105.^110.
The sky is falling. The sky is falling.
....^120.^125.^130.^135.^140.^145.^150.
Annotations
Id Type
SpanStart Span End Features
1 Addressee 4
24
2 Source
31
45
3 Date
53
69
ddmmyy=101194
4 Subject
78
98
5 Priority
109
115
6 Body
116
155
7 Sentence
116
135
8 Sentence
136
155
Table 5.3: Annotation showing overall document structure
In most cases, the hierarchical structure could be recovered from the spans. However, it may
be desirable to record this structure directly through a constituents feature whose value is
a sequence of annotations representing the immediate constituents of the initial annotation.
For the annotations of type parse, the constituents are either non-terminals (other annotations in the parse group) or tokens. For the sentence annotation, the constituents feature
points to the constituent tokens. A reference to another annotation is represented in the
table as "[ Annotation Id]"; for example, "[3]" represents a reference to annotation 3. Where
the value of an feature is a sequence of items, these items are separated by commas. No
special operations are provided in the current architecture for manipulating constituents. At
a less esoteric level, annotations can be used to record the overall structure of documents,
including in particular documents which have structured headers, as is shown in the third
example (Table 5.3).
If the Addressee, Source, ... annotations are recorded when the document is indexed for
retrieval, it will be possible to perform retrieval selectively on information in particular
fields. Our final example (Table 5.4) involves an annotation which effectively modifies the
document. The current architecture does not make any specific provision for the modification
Language Resources: Corpora, Documents and Annotations
Id
1
2
3
4
5
Type
token
token
token
token
token
95
Text
Topster tackles 2 terrorbytes.
^0...^5...^10..^15..^20..^25..
Annotations
SpanStart Span End Features
0
7
pos=NP correction=TIPSTER
8
15
pos=VBZ
16
17
pos=CD
18
29
pos=NNS correction=terabytes
29
30
Table 5.4: Annotation modifying the document
of the original text. However, some allowance must be made for processes such as spelling
correction. This information will be recorded as a correction feature on token annotations
and possibly on name annotations:
5.4.3
Creating, Viewing and Editing Diverse Annotation Types
Note that annotation types should consist of a single word with no spaces. Otherwise they
may not be recognised by other components such as JAPE transducers, and may create
problems when annotations are saved as inline (‘Save Preserving Format’ in the context
menu).
To view and edit annotation types, see Section 3.4. To add annotations of a new type, see
Section 3.4.5. To add a new annotation schema, see Section 3.4.6.
5.5
Document Formats
The following document formats are supported by GATE:
• Plain Text
• HTML
• SGML
• XML
• RTF
• Email
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Language Resources: Corpora, Documents and Annotations
• PDF (some documents)
• Microsoft Office (some formats)
• OpenOffice (some formats)
By default GATE will try and identify the type of the document, then strip and convert
any markup into GATE’s annotation format. To disable this process, set the markupAware
parameter on the document to false.
When reading a document of one of these types, GATE extracts the text between tags (where
such exist) and create a GATE annotation filled as follows:
The name of the tag will constitute the annotation’s type, all the tags attributes will materialize in the annotation’s features and the annotation will span over the text covered by the
tag. A few exceptions of this rule apply for the RTF, Email and Plain Text formats, which
will be described later in the input section of these formats.
The text between tags is extracted and appended to the GATE document’s content and all
annotations created from tags will be placed into a GATE annotation set named ‘Original
markups’.
Example:
If the markup is like this:
<aTagName attrib1="value1" attrib2="value2" attrib3="value3"> A
piece of text</aTagName>
then the annotation created by GATE will look like:
annotation.type = "aTagName";
annotation.fm = {attrib1=value1;atrtrib2=value2;attrib3=value3};
annotation.start = startNode;
annotation.end = endNode;
The startNode and endNode are created from offsets referring the beginning and the end of
‘A piece of text’ in the document’s content.
The documents supported by GATE have to be in one of the encodings accepted by Java.
The most popular is the ‘UTF-8’ encoding which is also the most storage efficient one for
UNICODE. If, when loading a document in GATE the encoding parameter is set to ‘’(the
empty string), then the default encoding of the platform will be used.
Language Resources: Corpora, Documents and Annotations
5.5.1
97
Detecting the Right Reader
In order to successfully apply the document creation algorithm described above, GATE
needs to detect the proper reader to use for each document format. If the user knows in
advance what kind of document they are loading then they can specify the MIME type (e.g.
text/html) using the init parameter mimeType, and GATE will respect this. If an explicit type
is not given, GATE attempts to determine the type by other means, taking into consideration
(where possible) the information provided by three sources:
• Document’s extension
• The web server’s content type
• Magic numbers detection
The first represents the extension of a file like (xml,htm,html,txt,sgm,rtf, etc), the second
represents the HTTP information sent by a web server regarding the content type of the
document being send by it (text/html; text/xml, etc), and the third one represents certain
sequences of chars which are ultimately number sequences. GATE is capable of supporting
multimedia documents, if the right reader is added to the framework. Sometimes, multimedia
documents are identified by a signature consisting in a sequence of numbers. Inside GATE
they are called magic numbers. For textual documents, certain char sequences form such
magic numbers. Examples of magic numbers sequences will be provided in the Input section
of each format supported by GATE.
All those tests are applied to each document read, and after that, a voting mechanism decides
what is the best reader to associate with the document. There is a degree of priority for all
those tests. The document’s extension test has the highest priority. If the system is in doubt
which reader to choose, then the one associated with document’s extension will be selected.
The next higher priority is given to the web server’s content type and the third one is given
to the magic numbers detection. However, any two tests that identify the same mime type,
will have the highest priority in deciding the reader that will be used. The web server test is
not always successful as there might be documents that are loaded from a local file system,
and the magic number detection test is not always applicable. In the next paragraphs we
will se how those tests are performed and what is the general mechanism behind reader
detection.
The method that detects the proper reader is a static one, and it belongs to the
gate.DocumentFormat class. It uses the information stored in the maps filled by the init()
method of each reader. This method comes with three signatures:
1
2
static public DocumentFormat ge tDocum entFor mat ( gate . Document
aGateDocument , URL url )
3
4
5
static public DocumentFormat ge tDocum entFor mat ( gate . Document
aGateDocument , String fileSuffix )
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6
7
8
static public DocumentFormat ge tDocum entFor mat ( gate . Document
aGateDocument , MimeType mimeType )
The first two methods try to detect the right MimeType for the GATE document, and after
that, they call the third one to return the reader associate with a MimeType. Of course, if an
explicit mimeType parameter was specified, GATE calls the third form of the method directly,
passing the specified type. GATE uses the implementation from ‘http://jigsaw.w3.org’ for
mime types.
The magic numbers test is performed using the information form
magic2mimeTypeMap map. Each key from this map, is searched in the first bufferSize (the
default value is 2048) chars of text. The method that does this is called
runMagicNumbers(InputStreamReader aReader) and it belongs to DocumentFormat class.
More details about it can be found in the GATE API documentation.
In order to activate a reader to perform the unpacking, the creole definition of a GATE
document defines a parameter called ‘markupAware’ initialized with a default value of true.
This parameter, forces GATE to detect a proper reader for the document being read. If no
reader is found, the document’s content is load and presented to the user, just like any other
text editor (this for textual documents).
The next subsections investigates particularities for each format and will describe the file
extensions registered with each document format.
5.5.2
XML
Input
GATE permits the processing of any XML document and offers support for XML namespaces.
It benefits the power of Apache’s Xerces parser and also makes use of Sun’s JAXP layer.
Changing the XML parser in GATE can be achieved by simply replacing the value of a Java
system property (‘javax.xml.parsers.SAXParserFactory’).
GATE will accept any well formed XML document as input. Although it has the possibility
to validate XML documents against DTDs it does not do so because the validating procedure
is time consuming and in many cases it issues messages that are annoying for the user.
There is an open problem with the general approach of reading XML, HTML and SGML
documents in GATE. As we previously said, the text covered by tags/elements is appended
to the GATE document content and a GATE annotation refers to this particular span of
text. When appending, in cases such as ‘end.</P><P>Start’ it might happen that the ending
word of the previous annotation is concatenated with the beginning phrase of the annotation
currently being created, resulting in a garbage input for GATE processing resources that
Language Resources: Corpora, Documents and Annotations
99
operate at the text surface.
Let’s take another example in order to better understand the problem:
<title>This is a title</title><p>This is a paragraph</p><a
href="#link">Here is an useful link</a>
When the markup is transformed to annotations, it is likely that the text from the document’s
content will be as follows:
This is a titleThis is a paragraphHere is an useful link
The annotations created will refer the right parts of the texts but for the GATE’s processing
resources like (tokenizer, gazetteer, etc) which work on this text, this will be a major disaster.
Therefore, in order to prevent this problem from happening, GATE checks if it’s likely to
join words and if this happens then it inserts a space between those words. So, the text will
look like this after loaded in GATE Developer:
This is a title This is a paragraph Here is an useful link
There are cases when these words are meant to be joined, but they are rare. This is why it’s
an open problem.
The extensions associate with the XML reader are:
• xml
• xhtm
• xhtml
The web server content type associate with xml documents is: text/xml.
The magic numbers test searches inside the document for the XML(<?xml version="1.0")
signature. It is also able to detect if the XML document uses the semantics described in the
GATE document format DTD (see 5.5.2 below) or uses other semantics.
Namespace handling
By default, GATE will retain the namespace prefix and namespace URIs of XML elements
when creating annotations and features within the Original markups annotation set. For
example, the element
<dc:title xmlns:dc="http://purl.org/dc/elements/1.1/">Document title</dc:title>
will create the following annotation
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dc:title(xmlns:dc=http://purl.org/dc/elements/1.1/)
However, as the colon character ’:’ is a reserved meta-character in JAPE, it is not possible
to write a JAPE rule that will match the dc:title element or its namespace URI.
If you need to match namespace-prefixed elements in the Original markups AS, you can alter
the default namespace deserialization behaviour to remove the namespace prefix and add it
as a feature (along with the namespace URI), by specifying the following attributes in the
<GATECONFIG> element of gate.xml or local configuration file:
• addNamespaceFeatures - set to ”true” to deserialize namespace prefix and uri information as features.
• namespaceURI - The feature name to use that will hold the namespace URI of the
element, e.g. ”namespace”
• namespacePrefix - The feature name to use that will hold the namespace prefix of
the element, e.g. ”prefix”
i.e.
<GATECONFIG
addNamespaceFeatures="true"
namespaceURI="namespace"
namespacePrefix="prefix" />
For example
<dc:title>Document title</dc:title>
would create in Original markups AS (assuming the xmlns:dc URI has defined in the document root or parent element)
title(prefix=dc, namespace=http://purl.org/dc/elements/1.1/)
If a JAPE rule is written to create a new annotation, e.g.
description(prefix=foo, namespace=http://www.example.org/)
then these would be serialized to
<dc:title xmlns:dc="http://purl.org/dc/elements/1.1/">Document title</dc:title>
<foo:description xmlns:foo="http://www.example.org/">...</foo:description>
when using the ’Save preserving document format’ XML output option (see 5.5.2 below).
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101
Output
GATE is capable of ensuring persistence for its resources. The types of persistent storage
used for Language Resources are:
• Java serialization;
• XML serialization.
We describe the latter case here.
XML persistence doesn’t necessarily preserve all the objects belonging to the annotations,
documents or corpora. Their features can be of all kinds of objects, with various layers of
nesting. For example, lists containing lists containing maps, etc. Serializing these arbitrary
data types in XML is not a simple task; GATE does the best it can, and supports native Java
types such as Integers and Booleans, but where complex data types are used, information
may be lost(the types will be converted into Strings). GATE provides a full serialization of
certain types of features such as collections, strings and numbers. It is possible to serialize
only those collections containing strings or numbers. The rest of other features are serialized
using their string representation and when read back, they will be all strings instead of being
the original objects. Consequences of this might be observed when performing evaluations
(see Chapter 10).
When GATE outputs an XML document it may do so in one of two ways:
• When the original document that was imported into GATE was an XML document,
GATE can dump that document back into XML (possibly with additional markup
added);
• For all document formats, GATE can dump its internal representation of the document
into XML.
In the former case, the XML output will be close to the original document. In the latter
case, the format is a GATE-specific one which can be read back by the system to recreate
all the information that GATE held internally for the document.
In order to understand why there are two types of XML serialization, one needs to understand
the structure of a GATE document. GATE allows a graph of annotations that refer to
parts of the text. Those annotations are grouped under annotation sets. Because of this
structure, sometimes it is impossible to save a document as XML using tags that surround
the text referred to by the annotation, because tags crossover situations could appear (XML
is essentially a tree-based model of information, whereas GATE uses graphs). Therefore, in
order to preserve all annotations in a GATE document, a custom type of XML document
was developed.
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The problem of crossover tags appears with GATE’s second option (the preserve format
one), which is implemented at the cost of losing certain annotations. The way it is applied
in GATE is that it tries to restore the original markup and where it is possible, to add in
the same manner annotations produced by GATE.
How to Access and Use the Two Forms of XML Serialization
Save as XML Option This option is available in GATE Developer in the pop-up menu
associated with each language resource (document or corpus). Saving a corpus as XML
is done by calling ‘Save as XML’ on each document of the corpus. This option saves all
the annotations of a document together their features(applying the restrictions previously
discussed), using the GateDocument.dtd :
<!ELEMENT GateDocument (GateDocumentFeatures,
TextWithNodes, (AnnotationSet+))>
<!ELEMENT GateDocumentFeatures (Feature+)>
<!ELEMENT Feature (Name, Value)>
<!ELEMENT Name (\#PCDATA)>
<!ELEMENT Value (\#PCDATA)>
<!ELEMENT TextWithNodes (\#PCDATA | Node)*>
<!ELEMENT AnnotationSet (Annotation*)>
<!ATTLIST AnnotationSet Name CDATA \#IMPLIED>
<!ELEMENT Annotation (Feature*)>
<!ATTLIST Annotation Type
CDATA \#REQUIRED
StartNode CDATA \#REQUIRED
EndNode
CDATA \#REQUIRED>
<!ELEMENT Node EMPTY>
<!ATTLIST Node id CDATA \#REQUIRED>
The document is saved under a name chosen by the user and it may have any extension.
However, the recommended extension would be ‘xml’.
Using GATE Embedded, this option is available by calling gate.Document’s toXml()
method. This method returns a string which is the XML representation of the document on
which the method was called.
Note: It is recommended that the string representation to be saved on the file system using the UTF-8 encoding, as the first line of the string is : <?xml version="1.0"
encoding="UTF-8"?>
Example of such a GATE format document:
<?xml version="1.0" encoding="UTF-8" ?>
Language Resources: Corpora, Documents and Annotations
<GateDocument>
<!-- The document’s features-->
<GateDocumentFeatures>
<Feature>
<Name className="java.lang.String">MimeType</Name>
<Value className="java.lang.String">text/plain</Value>
</Feature>
<Feature>
<Name className="java.lang.String">gate.SourceURL</Name>
<Value className="java.lang.String">file:/G:/tmp/example.txt</Value>
</Feature>
</GateDocumentFeatures>
<!-- The document content area with serialized nodes -->
<TextWithNodes>
<Node id="0"/>A TEENAGER <Node
id="11"/>yesterday<Node id="20"/> accused his parents of cruelty
by feeding him a daily diet of chips which sent his weight
ballooning to 22st at the age of l2<Node id="146"/>.<Node
id="147"/>
</TextWithNodes>
<!-- The default annotation set -->
<AnnotationSet>
<Annotation Type="Date" StartNode="11"
EndNode="20">
<Feature>
<Name className="java.lang.String">rule2</Name>
<Value className="java.lang.String">DateOnlyFinal</Value>
</Feature> <Feature>
<Name className="java.lang.String">rule1</Name>
<Value className="java.lang.String">GazDateWords</Value>
</Feature> <Feature>
<Name className="java.lang.String">kind</Name>
<Value className="java.lang.String">date</Value>
</Feature> </Annotation> <Annotation Type="Sentence" StartNode="0"
EndNode="147"> </Annotation> <Annotation Type="Split"
StartNode="146" EndNode="147"> <Feature>
<Name className="java.lang.String">kind</Name>
<Value className="java.lang.String">internal</Value>
</Feature> </Annotation> <Annotation Type="Lookup" StartNode="11"
EndNode="20"> <Feature>
<Name className="java.lang.String">majorType</Name>
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<Value className="java.lang.String">date_key</Value>
</Feature> </Annotation>
</AnnotationSet>
<!-- Named annotation set -->
<AnnotationSet Name="Original markups" >
<Annotation
Type="paragraph" StartNode="0" EndNode="147"> </Annotation>
</AnnotationSet>
</GateDocument>
Note: One must know that all features that are not collections containing numbers or strings
or that are not numbers or strings are discarded. With this option, GATE does not preserve
those features it cannot restore back.
The Preserve Format Option This option is available in GATE Developer from the
popup menu of the annotations table. If no annotation in this table is selected, then the
option will restore the document’s original markup. If certain annotations are selected, then
the option will attempt to restore the original markup and insert all the selected ones. When
an annotation violates the crossed over condition, that annotation is discarded and a message
is issued.
This option makes it possible to generate an XML document with tags surrounding the annotation’s referenced text and features saved as attributes. All features which are collections,
strings or numbers are saved, and the others are discarded. However, when read back, only
the attributes under the GATE namespace (see below) are reconstructed back differently to
the others. That is because GATE does not store in the XML document the information
about the features class and for collections the class of the items. So, when read back, all
features will become strings, except those under the GATE namespace.
One will notice that all generated tags have an attribute called ‘gateId’ under the namespace ‘http://www.gate.ac.uk’. The attribute is used when the document is read back in
GATE, in order to restore the annotation’s old ID. This feature is needed because it works
in close cooperation with another attribute under the same namespace, called ‘matches’.
This attribute indicates annotations/tags that refer the same entity1 . They are under this
namespace because GATE is sensitive to them and treats them differently to all other elements with their attributes which fall under the general reading algorithm described at the
beginning of this section.
The ‘gateId’ under GATE namespace is used to create an annotation which has as ID the
value indicated by this attribute. The ‘matches’ attribute is used to create an ArrayList in
1
It’s not an XML entity but a information extraction named entity
Language Resources: Corpora, Documents and Annotations
105
which the items will be Integers, representing the ID of annotations that the current one
matches.
Example:
If the text being processed is as follows:
<Person gate:gateId="23">John</Person> and <Person
gate:gateId="25" gate:matches="23;25;30">John Major</Person> are
the same person.
What GATE does when it parses this text is it creates two annotations:
a1.type = "Person"
a1.ID = Integer(23)
a1.start = <the start offset of
John>
a1.end = <the end offset of John>
a1.featureMap = {}
a2.type = "Person"
a2.ID = Integer(25)
a2.start = <the start offset
of John Major>
a2.end = <the end offset of John Major>
a2.featureMap = {matches=[Integer(23); Integer(25); Integer(30)]}
Under GATE Embedded, this option is available by calling gate.Document’s toXml(Set
aSetContainingAnnotations) method. This method returns a string which is the XML
representation of the document on which the method was called. If called with null as
a parameter, then the method will attempt to restore only the original markup. If the
parameter is a set that contains annotations, then each annotation is tested against the
crossover restriction, and for those found to violate it, a warning will be issued and they will
be discarded.
In the next subsections we will show how this option applies to the other formats supported
by GATE.
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5.5.3
HTML
Input
HTML documents are parsed by GATE using the NekoHTML parser. The documents are
read and created in GATE the same way as the XML documents.
The extensions associate with the HTML reader are:
• htm
• html
The web server content type associate with html documents is: text/html.
The magic numbers test searches inside the document for the HTML(<html) signature.There
are certain HTML documents that do not contain the HTML tag, so the magical numbers
test might not hold.
There is a certain degree of customization for HTML documents in that GATE introduces
new lines into the document’s text content in order to obtain a readable form. The annotations will refer the pieces of text as described in the original document but there will be a
few extra new line characters inserted.
After reading H1, H2, H3, H4, H5, H6, TR, CENTER, LI, BR and DIV tags, GATE will
introduce a new line (NL) char into the text. After a TITLE tag it will introduce two NLs.
With P tags, GATE will introduce one NL at the beginning of the paragraph and one at
the end of the paragraph. All newly added NLs are not considered to be part of the text
contained by the tag.
Output
The ‘Save as XML’ option works exactly the same for all GATE’s documents so there is no
particular observation to be made for the HTML formats.
When attempting to preserve the original markup formatting, GATE will generate the document in xhtml. The html document will look the same with any browser after processed
by GATE but it will be in another syntax.
Language Resources: Corpora, Documents and Annotations
5.5.4
107
SGML
Input
The SGML support in GATE is fairly light as there is no freely available Java SGML parser.
GATE uses a light converter attempting to transform the input SGML file into a well formed
XML. Because it does not make use of a DTD, the conversion might not be always good.
It is advisable to perform a SGML2XML conversion outside the system(using some other
specialized tools) before using the SGML document inside GATE.
The extensions associate with the SGML reader are:
• sgm
• sgml
The web server content type associate with xml documents is : text/sgml.
There is no magic numbers test for SGML.
Output
When attempting to preserve the original markup formatting, GATE will generate the document as XML because the real input of a SGML document inside GATE is an XML one.
5.5.5
Plain text
Input
When reading a plain text document, GATE attempts to detect its paragraphs and add
‘paragraph’ annotations to the document’s ‘Original markups’ annotation set. It does that
by detecting two consecutive NLs. The procedure works for both UNIX like or DOS like
text files.
Example:
If the plain text read is as follows:
Paragraph 1. This text belongs to the first paragraph.
Paragraph 2. This text belongs to the second paragraph
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Language Resources: Corpora, Documents and Annotations
then two ‘paragraph’ type annotation will be created in the ‘Original markups’ annotation
set (referring the first and second paragraphs ) with an empty feature map.
The extensions associate with the plain text reader are:
• txt
• text
The web server content type associate with plain text documents is: text/plain.
There is no magic numbers test for plain text.
Output
When attempting to preserve the original markup formatting, GATE will dump XML
markup that surrounds the text refereed.
The procedure described above applies both for plain text and RTF documents.
5.5.6
RTF
Input
Accessing RTF documents is performed by using the Java’s RTF editor kit. It only extracts
the document’s text content from the RTF document.
The extension associate with the RTF reader is ‘rtf ’.
The web server content type associate with xml documents is : text/rtf.
The magic numbers test searches for {\\rtf1.
Output
Same as the plain tex output.
Language Resources: Corpora, Documents and Annotations
5.5.7
109
Email
Input
GATE is able to read email messages packed in one document (UNIX mailbox format). It
detects multiple messages inside such documents and for each message it creates annotations
for all the fields composing an e-mail, like date, from, to, subject, etc. The message’s body
is analyzed and a paragraph detection is performed (just like in the plain text case) . All
annotation created have as type the name of the e-mail’s fields and they are placed in the
Original markup annotation set.
Example:
From [email protected] Wed Sep
6 10:35:50 2000
Date: Wed, 6 Sep2000 10:35:49 +0100 (BST)
From: forename1 surname2 <[email protected]>
To: forename2 surname2 <[email protected]>
Subject: A subject
Message-ID: <Pine.SOL.3.91.1000906103251.26010A-100000@servername>
MIME-Version: 1.0
Content-Type: TEXT/PLAIN; charset=US-ASCII
This text belongs to the e-mail body....
This is a paragraph in the body of the e-mail
This is another paragraph.
GATE attempts to detect lines such as ‘From [email protected] Wed Sep 6 10:35:50 2000’
in the e-mail text. Those lines separate e-mail messages contained in one file. After that,
for each field in the e-mail message annotations are created as follows:
The annotation type will be the name of the field, the feature map will be empty and the
annotation will span from the end of the field until the end of the line containing the e-mail
field.
Example:
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Language Resources: Corpora, Documents and Annotations
a1.type = "date" a1 spans between the two ^ ^. Date:^ Wed,
6Sep2000 10:35:49 +0100 (BST)^
a2.type = "from"; a2 spans between the two ^ ^. From:^ forename1
surname2 <[email protected]>^
The extensions associated with the email reader are:
• eml
• email
• mail
The web server content type associate with plain text documents is: text/email.
The magic numbers test searches for keywords like Subject:,etc.
Output
Same as plain text output.
5.5.8
PDF Files and Office Documents
GATE uses the Apache Tika library to provide support for PDF documents and a number of
the document formats from both Microsoft Office and OpenOffice. In essense Tika converts
the document structure into HTML which is then used to create a GATE document. This
means that whilst a PDF or Word document may have been loaded the “Original markups”
set will contain HTML elements. One advantage of this approach is that processing resources
and JAPE grammars designed for use with HTML files should also work well with PDF and
Office documents.
5.6
XML Input/Output
Support for input from and output to XML is described in Section 5.5.2. In short:
• GATE will read any well-formed XML document (it does not attempt to validate XML
documents). Markup will by default be converted into native GATE format.
• GATE will write back into XML in one of two ways:
Language Resources: Corpora, Documents and Annotations
111
1. Preserving the original format and adding selected markup (for example to add
the results of some language analysis process to the document).
2. In GATE’s own XML serialisation format, which encodes all the data in a GATE
Document (as far as this is possible within a tree-structured paradigm – for 100%
non-lossy data storage use GATE’s RDBMS or binary serialisation facilities – see
Section 4.5).
When using GATE Embedded, object representations of XML documents such as DOM or
jDOM, or query and transformation languages such as X-Path or XSLT, may be used in parallel
with GATE’s own Document representation (gate.Document) without conflicts.
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Chapter 6
ANNIE: a Nearly-New Information
Extraction System
And so the time had passed predictably and soberly enough in work and routine
chores, and the events of the previous night from first to last had faded; and only
now that both their days’ work was over, the child asleep and no further disturbance anticipated, did the shadowy figures from the masked ball, the melancholy
stranger and the dominoes in red, revive; and those trivial encounters became
magically and painfully interfused with the treacherous illusion of missed opportunities. Innocent yet ominous questions and vague ambiguous answers passed
to and fro between them; and, as neither of them doubted the other’s absolute
candour, both felt the need for mild revenge. They exaggerated the extent to
which their masked partners had attracted them, made fun of the jealous stirrings the other revealed, and lied dismissively about their own. Yet this light
banter about the trivial adventures of the previous night led to more serious discussion of those hidden, scarcely admitted desires which are apt to raise dark and
perilous storms even in the purest, most transparent soul; and they talked about
those secret regions for which they felt hardly any longing, yet towards which the
irrational wings of fate might one day drive them, if only in their dreams. For
however much they might belong to one another heart and soul, they knew last
night was not the first time they had been stirred by a whiff of freedom, danger
and adventure.
Dream Story, Arthur Schnitzler, 1926 (pp. 4-5).
GATE was originally developed in the context of Information Extraction (IE) R&D, and IE
systems in many languages and shapes and sizes have been created using GATE with the
IE components that have been distributed with it (see [Maynard et al. 00] for descriptions
of some of these projects).1
1
The principal architects of the IE systems in GATE version 1 were Robert Gaizauskas and Kevin
Humphreys. This work lives on in the LaSIE system. (A derivative of LaSIE was distributed with GATE
113
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ANNIE: a Nearly-New Information Extraction System
GATE is distributed with an IE system called ANNIE, A Nearly-New IE system (developed by Hamish Cunningham, Valentin Tablan, Diana Maynard, Kalina Bontcheva, Marin
Dimitrov and others). ANNIE relies on finite state algorithms and the JAPE language (see
Chapter 8).
ANNIE components form a pipeline which appears in figure 6.1. ANNIE components are
Figure 6.1: ANNIE and LaSIE
included with GATE (though the linguistic resources they rely on are generally more simple
than the ones we use in-house). The rest of this chapter describes these components.
6.1
Document Reset
The document reset resource enables the document to be reset to its original state, by removing all the annotation sets and their contents, apart from the one containing the document
format analysis (Original Markups). An optional parameter, keepOriginalMarkupsAS, allows users to decide whether to keep the Original Markups AS or not while reseting the
document. The parameter annotationTypes can be used to specify a list of annotation
types to remove from all the sets instead of the whole sets.
version 1 under the name VIE, a Vanilla IE system.)
ANNIE: a Nearly-New Information Extraction System
115
Alternatively, if the parameter setsToRemove is not empty, the other parameters except
annotationTypes are ignored and only the annotation sets specified in this list will be
removed. If annotationTypes is also specified, only those annotation types in the specified
sets are removed. In order to specify that you want to reset the default annotation set, just
click the ”Add” button without entering a name – this will add <null> which denotes the
default annotation set. This resource is normally added to the beginning of an application,
so that a document is reset before an application is rerun on that document.
6.2
Tokeniser
The tokeniser splits the text into very simple tokens such as numbers, punctuation and words
of different types. For example, we distinguish between words in uppercase and lowercase,
and between certain types of punctuation. The aim is to limit the work of the tokeniser
to maximise efficiency, and enable greater flexibility by placing the burden on the grammar
rules, which are more adaptable.
6.2.1
Tokeniser Rules
A rule has a left hand side (LHS) and a right hand side (RHS). The LHS is a regular
expression which has to be matched on the input; the RHS describes the annotations to be
added to the AnnotationSet. The LHS is separated from the RHS by ‘>’. The following
operators can be used on the LHS:
|
*
?
+
(or)
(0 or more occurrences)
(0 or 1 occurrences)
(1 or more occurrences)
The RHS uses ‘;’ as a separator, and has the following format:
{LHS} > {Annotation type};{attribute1}={value1};...;{attribute
n}={value n}
Details about the primitive constructs available are given in the tokeniser file (DefaultTokeniser.Rules).
The following tokeniser rule is for a word beginning with a single capital letter:
‘UPPERCASE_LETTER’ ‘LOWERCASE_LETTER’* >
Token;orth=upperInitial;kind=word;
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ANNIE: a Nearly-New Information Extraction System
It states that the sequence must begin with an uppercase letter, followed by zero or more
lowercase letters. This sequence will then be annotated as type ‘Token’. The attribute ‘orth’
(orthography) has the value ‘upperInitial’; the attribute ‘kind’ has the value ‘word’.
6.2.2
Token Types
In the default set of rules, the following kinds of Token and SpaceToken are possible:
Word
A word is defined as any set of contiguous upper or lowercase letters, including a hyphen
(but no other forms of punctuation). A word also has the attribute ‘orth’, for which four
values are defined:
• upperInitial - initial letter is uppercase, rest are lowercase
• allCaps - all uppercase letters
• lowerCase - all lowercase letters
• mixedCaps - any mixture of upper and lowercase letters not included in the above
categories
Number
A number is defined as any combination of consecutive digits. There are no subdivisions of
numbers.
Symbol
Two types of symbol are defined: currency symbol (e.g. ‘$’, ‘£’) and symbol (e.g. ‘&’, ‘ˆ’).
These are represented by any number of consecutive currency or other symbols (respectively).
Punctuation
Three types of punctuation are defined: start punctuation (e.g. ‘(’), end punctuation (e.g.
‘)’), and other punctuation (e.g. ‘:’). Each punctuation symbol is a separate token.
ANNIE: a Nearly-New Information Extraction System
117
SpaceToken
White spaces are divided into two types of SpaceToken - space and control - according to
whether they are pure space characters or control characters. Any contiguous (and homogeneous) set of space or control characters is defined as a SpaceToken.
The above description applies to the default tokeniser. However, alternative tokenisers can
be created if necessary. The choice of tokeniser is then determined at the time of text
processing.
6.2.3
English Tokeniser
The English Tokeniser is a processing resource that comprises a normal tokeniser and a JAPE
transducer (see Chapter 8). The transducer has the role of adapting the generic output of
the tokeniser to the requirements of the English part-of-speech tagger. One such adaptation
is the joining together in one token of constructs like “ ’30s”, “ ’Cause”, “ ’em”, “ ’N”, “
’S”, “ ’s”, “ ’T”, “ ’d”, “ ’ll”, “ ’m”, “ ’re”, “ ’til”, “ ve”, etc. Another task of the JAPE
transducer is to convert negative constructs like “don’t” from three tokens (“don”, “ ’ “ and
“t”) into two tokens (“do” and “n’t”).
The English Tokeniser should always be used on English texts that need to be processed
afterwards by the POS Tagger.
6.3
Gazetteer
The role of the gazetteer is to identify entity names in the text based on lists. The ANNIE
gazetteer is described here, and also covered in Chapter 13 in Section 13.2.
The gazetteer lists used are plain text files, with one entry per line. Each list represents a
set of names, such as names of cities, organisations, days of the week, etc.
Below is a small section of the list for units of currency:
Ecu
European Currency Units
FFr
Fr
German mark
German marks
New Taiwan dollar
New Taiwan dollars
NT dollar
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NT dollars
An index file (lists.def) is used to access these lists; for each list, a major type is specified
and, optionally, a minor type 2 . In the example below, the first column refers to the list
name, the second column to the major type, and the third to the minor type. These lists are
compiled into finite state machines. Any text tokens that are matched by these machines
will be annotated with features specifying the major and minor types. Grammar rules then
specify the types to be identified in particular circumstances. Each gazetteer list should
reside in the same directory as the index file.
currency_prefix.lst:currency_unit:pre_amount
currency_unit.lst:currency_unit:post_amount
date.lst:date:specific
day.lst:date:day
So, for example, if a specific day needs to be identified, the minor type ‘day’ should be
specified in the grammar, in order to match only information about specific days; if any kind
of date needs to be identified,the major type ‘date’ should be specified, to enable tokens
annotated with any information about dates to be identified. More information about this
can be found in the following section.
In addition, the gazetteer allows arbitrary feature values to be associated with particular
entries in a single list. ANNIE does not use this capability, but to enable it for your own
gazetteers, set the optional gazetteerFeatureSeparator parameter to a single character
(or an escape sequence such as \t or \uNNNN) when creating a gazetteer. In this mode, each
line in a .lst file can have feature values specified, for example, with the following entry in
the index file:
software_company.lst:company:software
the following software_company.lst:
Red Hat&stockSymbol=RHAT
Apple Computer&abbrev=Apple&stockSymbol=AAPL
Microsoft&abbrev=MS&stockSymbol=MSFT
and gazetteerFeatureSeparator set to &, the gazetteer will annotate Red Hat as a Lookup
with features majorType=company, minorType=software and stockSymbol=RHAT. Note that
you do not have to provide the same features for every line in the file, in particular it is
possible to provide extra features for some lines in the list but not others.
2
It is also possible to include a language in the same way, where lists for different languages are used,
though ANNIE is only concerned with monolingual recognition
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Here is a full list of the parameters used by the Default Gazetteer:
Init-time parameters
listsURL A URL pointing to the index file (usually lists.def) that contains the list of pattern
lists.
encoding The character encoding to be used while reading the pattern lists.
gazetteerFeatureSeparator The character used to add arbitrary features to gazetteer
entries. See above for an example.
caseSensitive Should the gazetteer be case sensitive during matching.
Run-time parameters
document The document to be processed.
annotationSetName The name for annotation set where the resulting Lookup annotations
will be created.
wholeWordsOnly Should the gazetteer only match whole words? If set to true, a string
segment in the input document will only be matched if it is bordered by characters
that are not letters, non spacing marks, or combining spacing marks (as identified by
the Unicode standard).
longestMatchOnly Should the gazetteer only match the longest possible string starting
from any position. This parameter is only relevant when the list of lookups contains
proper prefixes of other entries (e.g when both ‘Dell’ and ‘Dell Europe’ are in the lists).
The default behaviour (when this parameter is set to true) is to only match the longest
entry, ‘Dell Europe’ in this example. This is the default GATE gazetteer behaviour
since version 2.0. Setting this parameter to false will cause the gazetteer to match
all possible prefixes.
6.4
Sentence Splitter
The sentence splitter is a cascade of finite-state transducers which segments the text into
sentences. This module is required for the tagger. The splitter uses a gazetteer list of
abbreviations to help distinguish sentence-marking full stops from other kinds.
Each sentence is annotated with the type ‘Sentence’. Each sentence break (such as a full
stop) is also given a ‘Split’ annotation. It has a feature ‘kind’ with two possible values:
‘internal’ for any combination of exclamation and question mark or one to four dots and
‘external’ for a newline.
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The sentence splitter is domain and application-independent.
There is an alternative ruleset for the Sentence Splitter which considers newlines and carriage
returns differently. In general this version should be used when a new line on the page
indicates a new sentence). To use this alternative version, simply load the main-singlenl.jape from the default location instead of main.jape (the default file) when asked to select
the location of the grammar file to be used.
6.5
RegEx Sentence Splitter
The RegEx sentence splitter is an alternative to the standard ANNIE Sentence Splitter.
Its main aim is to address some performance issues identified in the JAPE-based splitter,
mainly do to with improving the execution time and robustness, especially when faced with
irregular input.
As its name suggests, the RegEx splitter is based on regular expressions, using the default
Java implementation.
The new splitter is configured by three files containing (Java style, see http://
java.sun.com/j2se/1.5.0/docs/api/java/util/regex/Pattern.html) regular expressions, one regex per line. The three different files encode patterns for:
internal splits sentence splits that are part of the sentence, such as sentence ending punctuation;
external splits sentence splits that are NOT part of the sentence, such as 2 consecutive
new lines;
non splits text fragments that might be seen as splits but they should be ignored (such as
full stops occurring inside abbreviations).
The new splitter comes with an initial set of patterns that try to emulate the behaviour of
the original splitter (apart from the situations where the original one was obviously wrong,
like not allowing sentences to start with a number).
Here is a full list of the parameters used by the RegEx Sentence Splitter:
Init-time parameters
encoding The character encoding to be used while reading the pattern lists.
externalSplitListURL URL for the file containing the list of external split patterns;
internalSplitListURL URL for the file containing the list of internal split patterns;
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nonSplitListURL URL for the file containing the list of non split patterns;
Run-time parameters
document The document to be processed.
outputASName The name for annotation set where the resulting Split and Sentence
annotations will be created.
6.6
Part of Speech Tagger
The tagger [Hepple 00] is a modified version of the Brill tagger, which produces a partof-speech tag as an annotation on each word or symbol. The list of tags used is given in
Appendix G. The tagger uses a default lexicon and ruleset (the result of training on a large
corpus taken from the Wall Street Journal). Both of these can be modified manually if
necessary. Two additional lexicons exist - one for texts in all uppercase (lexicon cap), and
one for texts in all lowercase (lexicon lower). To use these, the default lexicon should be
replaced with the appropriate lexicon at load time. The default ruleset should still be used
in this case.
The ANNIE Part-of-Speech tagger requires the following parameters.
• encoding - encoding to be used for reading rules and lexicons (init-time)
• lexiconURL - The URL for the lexicon file (init-time)
• rulesURL - The URL for the ruleset file (init-time)
• document - The document to be processed (run-time)
• inputASName - The name of the annotation set used for input (run-time)
• outputASName - The name of the annotation set used for output (run-time). This is
an optional parameter. If user does not provide any value, new annotations are created
under the default annotation set.
• baseTokenAnnotationType - The name of the annotation type that refers to Tokens in
a document (run-time, default = Token)
• baseSentenceAnnotationType - The name of the annotation type that refers to Sentences in a document (run-time, default = Sentences)
• outputAnnotationType - POS tags are added as category features on the annotations
of type ‘outputAnnotationType’ (run-time, default = Token)
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• failOnMissingInputAnnotations - if set to false, the PR will not fail with an ExecutionException if no input Annotations are found and instead only log a single warning
message per session and a debug message per document that has no input annotations
(run-time, default = true).
If - (inputASName == outputASName) AND (outputAnnotationType == baseTokenAnnotationType)
then - New features are added on existing annotations of type ‘baseTokenAnnotationType’.
otherwise - Tagger searches for the annotation of type ‘outputAnnotationType’ under the
‘outputASName’ annotation set that has the same offsets as that of the annotation with
type ‘baseTokenAnnotationType’. If it succeeds, it adds new feature on a found annotation, and otherwise, it creates a new annotation of type ‘outputAnnotationType’ under the
‘outputASName’ annotation set.
6.7
Semantic Tagger
ANNIE’s semantic tagger is based on the JAPE language – see Chapter 8. It contains rules
which act on annotations assigned in earlier phases, in order to produce outputs of annotated
entities.
6.8
Orthographic Coreference (OrthoMatcher)
(Note: this component was previously known as a ‘NameMatcher’.)
The Orthomatcher module adds identity relations between named entities found by the
semantic tagger, in order to perform coreference. It does not find new named entities as
such, but it may assign a type to an unclassified proper name, using the type of a matching
name.
The matching rules are only invoked if the names being compared are both of the same type,
i.e. both already tagged as (say) organisations, or if one of them is classified as ‘unknown’.
This prevents a previously classified name from being recategorised.
6.8.1
GATE Interface
Input – entity annotations, with an id attribute.
Output – matches attributes added to the existing entity annotations.
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6.8.2
123
Resources
A lookup table of aliases is used to record non-matching strings which represent the same
entity, e.g. ‘IBM’ and ‘Big Blue’, ‘Coca-Cola’ and ‘Coke’. There is also a table of spurious
matches, i.e. matching strings which do not represent the same entity, e.g. ‘BT Wireless’ and
‘BT Cellnet’ (which are two different organizations). The list of tables to be used is a load
time parameter of the orthomatcher: a default list is set but can be changed as necessary.
6.8.3
Processing
The wrapper builds an array of the strings, types and IDs of all name annotations, which is
then passed to a string comparison function for pairwise comparisons of all entries.
6.9
Pronominal Coreference
The pronominal coreference module performs anaphora resolution using the JAPE grammar
formalism. Note that this module is not automatically loaded with the other ANNIE modules, but can be loaded separately as a Processing Resource. The main module consists of
three submodules:
• quoted text module
• pleonastic it module
• pronominal resolution module
The first two modules are helper submodules for the pronominal one, because they do not
perform anything related to coreference resolution except the location of quoted fragments
and pleonastic it occurrences in text. They generate temporary annotations which are used
by the pronominal submodule (such temporary annotations are removed later).
The main coreference module can operate successfully only if all ANNIE modules were
already executed. The module depends on the following annotations created from the respective ANNIE modules:
• Token (English Tokenizer)
• Sentence (Sentence Splitter)
• Split (Sentence Splitter)
• Location (NE Transducer, OrthoMatcher)
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• Person (NE Transducer, OrthoMatcher)
• Organization (NE Transducer, OrthoMatcher)
For each pronoun (anaphor) the coreference module generates an annotation of type ‘Coreference’ containing two features:
• antecedent offset - this is the offset of the starting node for the annotation (entity)
which is proposed as the antecedent, or null if no antecedent can be proposed.
• matches - this is a list of annotation IDs that comprise the coreference chain comprising
this anaphor/antecedent pair.
6.9.1
Quoted Speech Submodule
The quoted speech submodule identifies quoted fragments in the text being analysed. The
identified fragments are used by the pronominal coreference submodule for the proper resolution of pronouns such as I, me, my, etc. which appear in quoted speech fragments. The
module produces ‘Quoted Text’ annotations.
The submodule itself is a JAPE transducer which loads a JAPE grammar and builds an
FSM over it. The FSM is intended to match the quoted fragments and generate appropriate
annotations that will be used later by the pronominal module.
The JAPE grammar consists of only four rules, which create temporary annotations for all
punctuation marks that may enclose quoted speech, such as ”, ’, ‘, etc. These rules then
try to identify fragments enclosed by such punctuation. Finally all temporary annotations
generated during the processing, except the ones of type ‘Quoted Text’, are removed (because
no other module will need them later).
6.9.2
Pleonastic It Submodule
The pleonastic it submodule matches pleonastic occurrences of ‘it’. Similar to the quoted
speech submodule, it is a JAPE transducer operating with a grammar containing patterns
that match the most commonly observed pleonastic it constructs.
6.9.3
Pronominal Resolution Submodule
The main functionality of the coreference resolution module is in the pronominal resolution
submodule. This uses the result from the execution of the quoted speech and pleonastic it
submodules. The module works according to the following algorithm:
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125
• Preprocess the current document. This step locates the annotations that the submodule need (such as Sentence, Token, Person, etc.) and prepares the appropriate data
structures for them.
• For each pronoun do the following:
– inspect the proper appropriate context for all candidate antecedents for this kind
of pronoun;
– choose the best antecedent (if any);
• Create the coreference chains from the individual anaphor/antecedent pairs and the
coreference information supplied by the OrthoMatcher (this step is performed from the
main coreference module).
6.9.4
Detailed Description of the Algorithm
Full details of the pronominal coreference algorithm are as follows.
Preprocessing
The preprocessing task includes the following subtasks:
• Identifying the sentences in the document being processed. The sentences are identified
with the help of the Sentence annotations generated from the Sentence Splitter. For
each sentence a data structure is prepared that contains three lists. The lists contain
the annotations for the person/organization/location named entities appearing in the
sentence. The named entities in the sentence are identified with the help of the Person,
Location and Organization annotations that are already generated from the Named
Entity Transducer and the OrthoMatcher.
• The gender of each person in the sentence is identified and stored in a global data
structure. It is possible that the gender information is missing for some entities - for
example if only the person family name is observed then the Named Entity transducer
will be unable to deduce the gender. In such cases the list with the matching entities
generated by the OrhtoMatcher is inspected and if some of the orthographic matches
contains gender information it is assigned to the entity being processed.
• The identified pleonastic it occurrences are stored in a separate list. The ‘Pleonastic
It’ annotations generated from the pleonastic submodule are used for the task.
• For each quoted text fragment, identified by the quoted text submodule, a special
structure is created that contains the persons and the 3rd person singular pronouns
such as ‘he’ and ‘she’ that appear in the sentence containing the quoted text, but not
in the quoted text span (i.e. the ones preceding and succeeding the quote).
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Pronoun Resolution
This task includes the following subtasks:
Retrieving all the pronouns in the document. Pronouns are represented as annotations of
type ‘Token’ with feature ‘category’ having value ‘PRP$’ or ‘PRP’. The former classifies
possessive adjectives such as my, your, etc. and the latter classifies personal, reflexive etc.
pronouns. The two types of pronouns are combined in one list and sorted according to their
offset in the text.
For each pronoun in the list the following actions are performed:
• If the pronoun is ‘it’, then the module performs a check to determine if this is a
pleonastic occurrence. If it is, then no further attempt for resolution is made.
• The proper context is determined. The context size is expressed in the number of
sentences it will contain. The context always includes the current sentence (the one
containing the pronoun), the preceding sentence and zero or more preceding sentences.
• Depending on the type of pronoun, a set of candidate antecedents is proposed. The
candidate set includes the named entities that are compatible with this pronoun. For
example if the current pronoun is she then only the Person annotations with ‘gender’
feature equal to ‘female’ or ‘unknown’ will be considered as candidates.
• From all candidates, one is chosen according to evaluation criteria specific for the
pronoun.
Coreference Chain Generation
This step is actually performed by the main module. After executing each of the submodules
on the current document, the coreference module follows the steps:
• Retrieves the anaphor/antecedent pairs generated from them.
• For each pair, the orthographic matches (if any) of the antecedent entity is retrieved
and then extended with the anaphor of the pair (i.e. the pronoun). The result is
the coreference chain for the entity. The coreference chain contains the IDs of the
annotations (entities) that co-refer.
• A new Coreference annotation is created for each chain. The annotation contains a
single feature ‘matches’ whose value is the coreference chain (the list with IDs). The
annotations are exported in a pre-specified annotation set.
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127
The resolution of she, her, her$, he, him, his, herself and himself are similar because an
analysis of a corpus showed that these pronouns are related to their antecedents in a similar
manner. The characteristics of the resolution process are:
• Context inspected is not very big - cases where the antecedent is found more than 3
sentences back from the anaphor are rare.
• Recency factor is heavily used - the candidate antecedents that appear closer to the
anaphor in the text are scored better.
• Anaphora have higher priority than cataphora. If there is an anaphoric candidate and
a cataphoric one, then the anaphoric one is preferred, even if the recency factor scores
the cataphoric candidate better.
The resolution process performs the following steps:
• Inspect the context of the anaphor for candidate antecedents. Every Person annotation
is consider to be a candidate. Cases where she/her refers to inanimate entity (ship for
example) are not handled.
• For each candidate perform a gender compatibility check - only candidates having
‘gender’ feature equal to ‘unknown’ or compatible with the pronoun are considered for
further evaluation.
• Evaluate each candidate with the best candidate so far. If the two candidates are
anaphoric for the pronoun then choose the one that appears closer. The same holds
for the case where the two candidates are cataphoric relative to the pronoun. If one is
anaphoric and the other is cataphoric then choose the former, even if the latter appears
closer to the pronoun.
Resolution of ‘it’, ‘its’, ‘itself ’
This set of pronouns also shares many common characteristics. The resolution process contains certain differences with the one for the previous set of pronouns. Successful resolution
for it, its, itself is more difficult because of the following factors:
• There is no gender compatibility restriction. In the case in which there are several
candidates in the context, the gender compatibility restriction is very useful for rejecting some of the candidates. When no such restriction exists, and with the lack of
any syntactic or ontological information about the entities in the context, the recency
factor plays the major role in choosing the best antecedent.
• The number of nominal antecedents (i.e. entities that are not referred by name) is
much higher compared to the number of such antecedents for she, he, etc. In this case
trying to find an antecedent only amongst named entities degrades the precision a lot.
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Resolution of ‘I’, ‘me’, ‘my’, ‘myself ’
Resolution of these pronouns is dependent on the work of the quoted speech submodule.
One important difference from the resolution process of other pronouns is that the context
is not measured in sentences but depends solely on the quote span. Another difference is
that the context is not contiguous - the quoted fragment itself is excluded from the context,
because it is unlikely that an antecedent for I, me, etc. appears there. The context itself
consists of:
• the part of the sentence where the quoted fragment originates, that is not contained
in the quote - i.e. the text prior to the quote;
• the part of the sentence where the quoted fragment ends, that is not contained in the
quote - i.e. the text following the quote;
• the part of the sentence preceding the sentence where the quote originates, which is
not included in other quote.
It is worth noting that contrary to other pronouns, the antecedent for I, me, my and myself is
most often cataphoric or if anaphoric it is not in the same sentence with the quoted fragment.
The resolution algorithm consists of the following steps:
• Locate the quoted fragment description that contains the pronoun. If the pronoun is
not contained in any fragment then return without proposing an antecedent.
• Inspect the context for the quoted fragment (as defined above) for candidate antecedents. Candidates are considered annotations of type Pronoun or annotations
of type Token with features category = ‘PRP’, string = ‘she’ or category = ‘PRP’,
string = ‘he’.
• Try to locate a candidate in the text succeeding the quoted fragment (first pattern).
If more than one candidate is present, choose the closest to the end of the quote. If a
candidate is found then propose it as antecedent and exit.
• Try to locate a candidate in the text preceding the quoted fragment (third pattern).
Choose the closest one to the beginning of the quote. If found then set as antecedent
and exit.
• Try to locate antecedents in the unquoted part of the sentence preceding the sentence
where the quote starts (second pattern). Give preference to the one closest to the end
of the quote (if any) in the preceding sentence or closest to the sentence beginning.
ANNIE: a Nearly-New Information Extraction System
6.10
129
A Walk-Through Example
Let us take an example of a 3-stage procedure using the tokeniser, gazetteer and namedentity grammar. Suppose we wish to recognise the phrase ‘800,000 US dollars’ as an entity
of type ‘Number’, with the feature ‘money’.
First of all, we give an example of a grammar rule (and corresponding macros) for money,
which would recognise this type of pattern.
Macro: MILLION_BILLION
({Token.string == "m"}|
{Token.string == "million"}|
{Token.string == "b"}|
{Token.string == "billion"}
)
Macro: AMOUNT_NUMBER
({Token.kind == number}
(({Token.string == ","}|
{Token.string == "."})
{Token.kind == number})*
(({SpaceToken.kind == space})?
(MILLION_BILLION)?)
)
Rule: Money1
// e.g. 30 pounds
(
(AMOUNT_NUMBER)
(SpaceToken.kind == space)?
({Lookup.majorType == currency_unit})
)
:money -->
:money.Number = {kind = "money", rule = "Money1"}
6.10.1
Step 1 - Tokenisation
The tokeniser separates this phrase into the following tokens. In general, a word is comprised
of any number of letters of either case, including a hyphen, but nothing else; a number is
composed of any sequence of digits; punctuation is recognised individually (each character
is a separate token), and any number of consecutive spaces and/or control characters are
recognised as a single spacetoken.
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Token, string = ‘800’, kind = number, length = 3
Token, string = ‘,’, kind = punctuation, length = 1
Token, string = ‘000’, kind = number, length = 3
SpaceToken, string = ‘ ’, kind = space, length = 1
Token, string = ‘US’, kind = word, length = 2, orth = allCaps
SpaceToken, string = ‘ ’, kind = space, length = 1
Token, string = ‘dollars’, kind = word, length = 7, orth = lowercase
6.10.2
Step 2 - List Lookup
The gazetteer lists are then searched to find all occurrences of matching words in the text.
It finds the following match for the string ‘US dollars’:
Lookup, minorType = post_amount, majorType = currency_unit
6.10.3
Step 3 - Grammar Rules
The grammar rule for money is then invoked. The macro MILLION BILLION recognises
any of the strings ‘m’, ‘million’, ‘b’, ‘billion’. Since none of these exist in the text, it passes
onto the next macro. The AMOUNT NUMBER macro recognises a number, optionally
followed by any number of sequences of the form‘dot or comma plus number’, followed
by an optional space and an optional MILLION BILLION. In this case, ‘800,000’ will be
recognised. Finally, the rule Money1 is invoked. This recognises the string identified by the
AMOUNT NUMBER macro, followed by an optional space, followed by a unit of currency
(as determined by the gazetteer). In this case, ‘US dollars’ has been identified as a currency
unit, so the rule Money1 recognises the entire string ‘800,000 US dollars’. Following the rule,
it will be annotated as a Number entity of type Money:
Number, kind = money, rule = Money1
Part II
GATE for Advanced Users
131
Chapter 7
GATE Embedded
7.1
Quick Start with GATE Embedded
Embedding GATE-based language processing in other applications using GATE Embedded
(the GATE API) is straightforward:
• add $GATE_HOME/bin/gate.jar and the JAR files in $GATE_HOME/lib to the Java
CLASSPATH ($GATE_HOME is the GATE root directory)
• tell Java that the GATE Unicode Kit is an extension:
-Djava.ext.dirs=$GATE_HOME/lib/ext
N.B. This is only necessary for GUI applications that need to support Unicode text
input; other applications such as command line or web applications don’t generally
need GUK.
• initialise GATE with gate.Gate.init();
• program to the framework API.
For example, this code will create the ANNIE extraction system:
1
2
// initialise the GATE library
Gate . init ();
3
4
5
6
7
8
// load ANNIE as an application from a gapp file
S e r i a l A n a l y s e r C o n t r o l l e r controller = ( S e r i a l A n a l y s e r C o n t r o l l e r )
Pe rs is te nc eM an ag er . l oad Ob je ct Fr om Fi le ( new File ( new File (
Gate . getPluginsHome () , ANNIEConstants . PLUGIN_DIR ) ,
ANNIEConstants . DEFAULT_FILE ));
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GATE Embedded
If you want to use resources from any plugins, you need to load the plugins before calling
createResource:
Gate . init ();
1
2
// need Tools plugin for the Morphological analyser
Gate . ge tCreol eRegis ter (). r eg i s te r D ir e c to r i es (
new File ( Gate . getPluginsHome () , " Tools " ). toURL ()
);
3
4
5
6
7
...
8
9
P roc es si ng Re so ur ce morpher = ( P ro ces si ng Re so ur ce )
Factory . createResource ( " gate . creole . morph . Morph " );
10
11
Instead of creating your processing resources individually using the Factory, you can create
your application in GATE Developer, save it using the ‘save application state’ option (see
Section 3.8.3), and then load the saved state from your code. This will automatically reload
any plugins that were loaded when the state was saved, you do not need to load them
manually.
Gate . init ();
1
2
CorpusController controller = ( CorpusController )
P ersis te nc eM an ag er . l oa dOb je ct Fr om Fi le ( new File ( " savedState . xgapp " ));
3
4
5
// loadObjectFromUrl is also available
6
There are many examples of using GATE Embedded available at:
http://gate.ac.uk/wiki/code-repository/.
See Section 2.3 for details of the system properties GATE uses to find its configuration files.
7.2
Resource Management in GATE Embedded
As outlined earlier, GATE defines three different types of resources:
Language Resources : (LRs) entities that hold linguistic data.
Processing Resources : (PRs) entities that process data.
Visual Resources : (VRs) components used for building graphical interfaces.
These resources are collectively named CREOLE1 resources.
1
CREOLE stands for Collection of REusable Objects for Language Engineering
GATE Embedded
135
All CREOLE resources have some associated meta-data in the form of an entry in a special
XML file named creole.xml. The most important role of that meta-data is to specify the set
of parameters that a resource understands, which of them are required and which not, if they
have default values and what those are. The valid parameters for a resource are described
in the resource’s section of its creole.xml file or in Java annotations on the resource class
– see Section 4.7.
All resource types have creation-time parameters that are used during the initialisation
phase. Processing Resources also have run-time parameters that get used during execution
(see Section 7.5 for more details).
Controllers are used to define GATE applications and have the role of controlling the
execution flow (see Section 7.6 for more details).
This section describes how to create and delete CREOLE resources as objects in a running
Java virtual machine. This process involves using GATE’s Factory class2 , and, in the case
of LRs, may also involve using a DataStore.
CREOLE resources are Java Beans; creation of a resource object involves using a default
constructor, then setting parameters on the bean, then calling an init() method. The
Factory takes care of all this, makes sure that the GATE Developer GUI is told about what
is happening (when GUI components exist at runtime), and also takes care of restoring LRs
from DataStores. A programmer using GATE Embedded should never call the
constructor of a resource: always use the Factory!
Creating a resource involves providing the following information:
• fully qualified class name for the resource. This is the only required value. For
all the rest, defaults will be used if actual values are not provided.
• values for the creation time parameters.†
• initial values for resource features.† For an explanation on features see Section 7.4.2.
• a name for the new resource;
†
Parameters and features need to be provided in the form of a GATE Feature Map which is
essentially a java Map (java.util.Map) implementation, see Section 7.4.2 for more details
on Feature Maps.
Creating a resource via the Factory involves passing values for any create-time parameters
that require setting to the Factory’s createResource method. If no parameters are passed,
the defaults are used. So, for example, the following code creates a default ANNIE part-ofspeech tagger:
2
Fully qualified name: gate.Factory
136
GATE Embedded
Gate . ge tCreol eRegis ter (). r eg i s te r D ir e c to r i es ( new File (
Gate . getPluginsHome () , ANNIEConstants . PLUGIN_DIR ). toURI (). toURL ());
FeatureMap params = Factory . newFeatureMap (); //empty map:default params
Pr oc es si ng Re so ur ce tagger = ( Pr oc es si ng Re so ur ce )
Factory . createResource ( " gate . creole . POSTagger " , params );
1
2
3
4
5
Note that if the resource created here had any parameters that were both mandatory and
had no default value, the createResource call would throw an exception. In this case, all
the information needed to create a tagger is available in default values given in the tagger’s
XML definition (in plugins/ANNIE/creole.xml):
<RESOURCE>
<NAME>ANNIE POS Tagger</NAME>
<COMMENT>Mark Hepple’s Brill-style POS tagger</COMMENT>
<CLASS>gate.creole.POSTagger</CLASS>
<PARAMETER NAME="document"
COMMENT="The document to be processed"
RUNTIME="true">gate.Document</PARAMETER>
....
<PARAMETER NAME="rulesURL" DEFAULT="resources/heptag/ruleset"
COMMENT="The URL for the ruleset file"
OPTIONAL="true">java.net.URL</PARAMETER>
</RESOURCE>
Here the two parameters shown are either ‘runtime’ parameters, which are set before a PR is
executed, or have a default value (in this case the default rules file is distributed with GATE
itself).
When creating a Document, however, the URL of the source for the document must be
provided3 . For example:
URL u = new URL ( " http :// gate . ac . uk / hamish / " );
FeatureMap params = Factory . newFeatureMap ();
params . put ( " sourceUrl " , u );
Document doc = ( Document )
Factory . createResource ( " gate . corpora . DocumentImpl " , params );
1
2
3
4
5
Note that the document created here is transient: when you quit the JVM the document
will no longer exist. If you want the document to be persistent, you need to store it in a
DataStore (see Section 7.4.5).
Apart from createResource() methods with different signatures, Factory also provides
some shortcuts for common operations, listed in table 7.1.
GATE maintains various data structures that allow the retrieval of loaded resources. When
a resource is no longer required, it needs to be removed from those structures in order to
3
Alternatively a string giving the document source may be provided.
GATE Embedded
137
Method
newFeatureMap()
newDocument(String content)
newDocument(URL sourceUrl)
newDocument(URL sourceUrl,
String encoding)
newCorpus(String name)
Purpose
Creates a new Feature Map (as used in
the example above).
Creates a new GATE Document starting from a String value that will be used
to generate the document content.
Creates a new GATE Document using
the text pointed by an URL to generate
the document content.
Same as above but allows the specification of an encoding to be used while
downloading the document content.
creates a new GATE Corpus with a
specified name.
Table 7.1: Factory Operations
remove all references to it, thus making it a candidate for garbage collection. This is achieved
using the deleteResource(Resource res) method on Factory.
Simply removing all references to a resource from the user code will NOT be enough to
make the resource collect-able. Not calling Factory.deleteResource() will lead to memory
leaks!
7.3
Using CREOLE Plugins
As shown in the examples above, in order to use a CREOLE resource the relevant CREOLE
plugin must be loaded. Processing Resources, Visual Resources and Language Resources
other than Document, Corpus and DataStore all require that the appropriate plugin is first
loaded. When using Document, Corpus or DataStore, you do not need to first load a plugin.
The following API calls listed in table 7.2 are relevant to working with CREOLE plugins.
If you are writing a GATE Embedded application and have a single resource class
that will only be used from your embedded code (and so does not need to be distributed as a complete plugin), and all the configuration for that resource is provided
as Java annotations on the class, then it is possible to register the class with the
CreoleRegister at runtime without needing to package it in a JAR and provide a
creole.xml file. You can pass the Class object representing your resource class to
Gate.getCreoleRegister().registerComponent() method and then create instances of
the resource in the usual way using Factory.createResource. Note that resources cannot
be registered this way in the developer GUI, and cannot be included in saved application
states (see section 7.8 below).
138
GATE Embedded
Class gate.Gate
Method
Purpose
public static void addKnownadds the plugin to the list of known pluPlugin(URL pluginURL)
gins.
public static void removetells the system to ‘forget’ about one
KnownPlugin(URL pluginURL)
previously known directory. If the specified directory was loaded, it will be unloaded as well - i.e. all the metadata
relating to resources defined by this directory will be removed from memory.
public static void addAutoload- adds a new directory to the list of pluPlugin(URL pluginUrl)
gins that are loaded automatically at
start-up.
public static void removeAutells the system to remove a plugin URL
toloadPlugin(URL pluginURL)
from the list of plugins that are loaded
automatically at system start-up. This
will be reflected in the user’s configuration data file.
Class gate.CreoleRegister
public void registerDirectoloads a new CREOLE directory. The
ries(URL directoryUrl)
new plugin is added to the list of known
plugins if not already there.
public void registerComporegisters a single @CreoleResource annent(Class<? extends Resource>
notated class without the need for a
cls)
creole.xml file.
public void removeDirectory(URL unloads a loaded CREOLE plugin.
directory)
Table 7.2: Calls Relevant to CREOLE Plugins
GATE Embedded
7.4
139
Language Resources
This section describes the implementation of documents and corpora in GATE.
7.4.1
GATE Documents
Documents are modelled as content plus annotations (see Section 7.4.4) plus features (see
Section 7.4.2).
The
content
of
a
document
can
be
any
implementation
of
the
gate.DocumentContent interface; the features are <attribute, value> pairs stored a Feature
Map. Attributes are String values while the values can be any Java object.
The annotations are grouped in sets (see section 7.4.3). A document has a default (anonymous) annotations set and any number of named annotations sets.
Documents are defined by the gate.Document interface and there is also a provided implementation:
gate.corpora.DocumentImpl : transient document. Can be stored persistently through
Java serialisation.
Main Document functions are presented in table 7.3.
7.4.2
Feature Maps
All CREOLE resources as well as the Controllers and the annotations can have attached
meta-data in the form of Feature Maps.
A Feature Map is a Java Map (i.e. it implements the java.util.Map interface) and holds
<attribute-name, attribute-value> pairs. The attribute names are Strings while the values
can be any Java Objects.
The use of non-Serialisable objects as values is strongly discouraged.
Feature Maps are created using the gate.Factory.newFeatureMap() method.
The
actual
implementation
for
gate.util.SimpleFeatureMapImpl class.
FeatureMaps
is
provided
by
the
Objects that have features in GATE implement the gate.util.FeatureBearer interface which has only the two accessor methods for the object features: FeatureMap
getFeatures() and void setFeatures(FeatureMap features).
140
GATE Embedded
Content Manipulation
Method
Purpose
DocumentContent getContent()
Gets the Document content.
void edit(Long start, Long end,
Modifies the Document content.
DocumentContent replacement)
void setContent(DocumentContent
Replaces the entire content.
newContent)
Annotations Manipulation
Method
Purpose
public AnnotationSet getAnnotaReturns the default annotation set.
tions()
public AnnotationSet getAnnotaReturns a named annotation set.
tions(String name)
public Map getNamedAnnotation- Returns all the named annotation sets.
Sets()
void removeAnnotationRemoves a named annotation set.
Set(String name)
Input Output
String toXml()
Serialises the Document in XML format.
String toXml(Set
Generates XML from a set of annotaaSourceAnnotationSet, boolean
tions only, trying to preserve the origiincludeFeatures)
nal format of the file used to create the
document.
Table 7.3: gate.Document methods.
GATE Embedded
141
Getting a particular feature from an object
¯
1
2
3
4
5
6
7
Object obj ;
String featureName = " length " ;
if ( obj instanceof FeatureBearer ){
FeatureMap features = (( FeatureBearer ) obj ). getFeatures ();
Object value = ( features == null ) ? null :
features . get ( featureName );
}
7.4.3
Annotation Sets
A GATE document can have one or more annotation layers — an anonymous one, (also
called default), and as many named ones as necessary.
An annotation layer is organised as a Directed Acyclic Graph (DAG) on which the nodes
are particular locations —anchors— in the document content and the arcs are made out of
annotations reaching from the location indicated by the start node to the one pointed by the
end node (see Figure 7.1 for an illustration). Because of the graph metaphor, the annotation
layers are also called annotation graphs. In terms of Java objects, the annotation layers are
represented using the Set paradigm as defined by the collections library and they are hence
named annotation sets. The terms of annotation layer, graph and set are interchangeable
and refer to the same concept when used in this book.
Figure 7.1: The Annotation Graph model.
An annotation set holds a number of annotations and maintains a series of indices in order
to provide fast access to the contained annotations.
The GATE Annotation Sets are defined by the gate.AnnotationSet interface and there is
a default implementation provided:
gate.annotation.AnnotationSetImpl annotation set implementation used by transient
documents.
The annotation sets are created by the document as required. The first time a particular
annotation set is requested from a document it will be transparently created if it doesn’t
exist.
142
GATE Embedded
Annotations Manipulation
Method
Purpose
Integer add(Long start, Long
Creates a new annotation between two
end, String type, FeatureMap
offsets, adds it to this set and returns
features)
its id.
Integer add(Node start, Node
Creates a new annotation between two
end, String type, FeatureMap
nodes, adds it to this set and returns its
features)
id.
boolean remove(Object o)
Removes an annotation from this set.
Nodes
Method
Purpose
Node firstNode()
Gets the node with the smallest offset.
Node lastNode()
Gets the node with the largest offset.
Node nextNode(Node node)
Get the first node that is relevant for
this annotation set and which has the
offset larger than the one of the node
provided.
Set implementation
Iterator iterator()
int size()
Table 7.4: gate.AnnotationSet methods (general purpose).
Tables 7.4 and 7.5 list the most used Annotation Set functions.
Iterating from left to right over all annotations of a given type
¯
1
2
3
4
5
6
7
8
9
10
11
12
AnnotationSet annSet = ...;
String type = " Person " ;
//Get all person annotations
AnnotationSet persSet = annSet . get ( type );
//Sort the annotations
List persList = new ArrayList ( persSet );
Collections . sort ( persList , new gate . util . OffsetComparator ());
//Iterate
Iterator persIter = persList . iterator ();
while ( persIter . hasNext ()){
...
}
7.4.4
Annotations
An annotation, is a form of meta-data attached to a particular section of document content.
The connection between the annotation and the content it refers to is made by means of two
pointers that represent the start and end locations of the covered content. An annotation
GATE Embedded
143
Searching
AnnotationSet get(Long offset)
Select annotations by offset. This returns the set of annotations whose start
node is the least such that it is less than
or equal to offset. If a positional index
doesn’t exist it is created. If there are
no nodes at or beyond the offset parameter then it will return null.
AnnotationSet get(Long
Select annotations by offset. This restartOffset, Long endOffset)
turns the set of annotations that overlap
totally or partially with the interval defined by the two provided offsets. The
result will include all the annotations
that either:
• start before the start offset and
end strictly after it
• start at a position between the
start and the end offsets
AnnotationSet get(String type)
AnnotationSet get(Set types)
AnnotationSet get(String type,
FeatureMap constraints)
Set getAllTypes()
AnnotationSet getContained(Long
startOffset, Long endOffset)
AnnotationSet getCovering(String
neededType, Long startOffset,
Long endOffset)
Returns all annotations of the specified
type.
Returns all annotations of the specified
types.
Selects annotations by type and features.
Gets a set of java.lang.String objects
representing all the annotation types
present in this annotation set.
Select annotations contained within an
interval, i.e.
Select annotations of the given type
that completely span the range.
Table 7.5: gate.AnnotationSet methods (searching).
144
GATE Embedded
must also have a type (or a name) which is used to create classes of similar annotations,
usually linked together by their semantics.
An Annotation is defined by:
start node a location in the document content defined by an offset.
end node a location in the document content defined by an offset.
type a String value.
features (see Section 7.4.2).
ID an Integer value. All annotations IDs are unique inside an annotation set.
In GATE Embedded, annotations are defined by the gate.Annotation interface and implemented by the gate.annotation.AnnotationImpl class. Annotations exist only as members
of annotation sets (see Section 7.4.3) and they should not be directly created by means of a
constructor. Their creation should always be delegated to the containing annotation set.
7.4.5
GATE Corpora
A corpus in GATE is a Java List (i.e. an implementation of java.util.List) of documents.
GATE corpora are defined by the gate.Corpus interface and the following implementations
are available:
gate.corpora.CorpusImpl used for transient corpora.
gate.corpora.SerialCorpusImpl used for persistent corpora that are stored in a serial
datastore (i.e. as a directory in a file system).
Apart from implementation for the standard List methods, a Corpus also implements the
methods in table 7.6.
Creating a corpus from all XML files in a directory
1
2
3
4
5
Corpus corpus = Factory . newCorpus ( " My XML Files " );
File directory = ...;
E x te n s io n F il e F il t e r filter = new E x te n s io n F il e F il t e r ( " XML files " , " xml " );
URL url = directory . toURL ();
corpus . populate ( url , filter , null , false );
Using a DataStore
Assuming that you have a DataStore already open called myDataStore, this code will ask
the datastore to take over persistence of your document, and to synchronise the memory
representation of the document with the disk storage:
GATE Embedded
145
Method
String getDocumentName(int
index)
List getDocumentNames()
void populate(URL directory,
FileFilter filter,
String encoding, boolean
recurseDirectories)
void populate(URL
singleConcatenatedFile,
String documentRootElement,
String encoding, int
numberOfDocumentsToExtract,
String documentNamePrefix,
DocType documentType)
Purpose
Gets the name of a document in this
corpus.
Gets the names of all the documents in
this corpus.
Fills this corpus with documents created on the fly from selected files
in a directory. Uses a FileFilter
to select which files will be used
and which will be ignored. A simple file filter based on extensions
is provided in the Gate distribution
(gate.util.ExtensionFileFilter).
Fills the provided corpus with documents extracted from the provided
single concatenated file.
Uses the
content between the start and
end of the element as specified by
documentRootElement for each document. The parameter documentType
specifies if the resulting files are html,
xml or of any other type. User can also
restrict the number of documents to
extract by providing the relevant value
for
numberOfDocumentsToExtract
parameter.
Table 7.6: gate.Corpus methods.
146
GATE Embedded
Document persistentDoc = myDataStore.adopt(doc, mySecurity);
myDataStore.sync(persistentDoc);
When you want to restore a document (or other LR) from a datastore, you make the same
createResource call to the Factory as for the creation of a transient resource, but this time
you tell it the datastore the resource came from, and the ID of the resource in that datastore:
1
2
3
URL u = ....; // URL of a serial datastore directory
SerialDataStore sds = new SerialDataStore ( u . toString ());
sds . open ();
4
5
6
// getLrIds returns a list of LR Ids, so we get the first one
Object lrId = sds . getLrIds ( " gate . corpora . DocumentImpl " ). get (0);
7
8
9
10
11
12
13
// we need to tell the factory about the LR’s ID in the data
// store, and about which datastore it is in - we do this
// via a feature map:
FeatureMap features = Factory . newFeatureMap ();
features . put ( DataStore . LR_ID_FEATURE_NAME , lrId );
features . put ( DataStore . DATASTORE_FEATURE_NAME , sds );
14
15
16
17
7.5
// read the document back
Document doc = ( Document )
Factory . createResource ( " gate . corpora . DocumentImpl " , features );
Processing Resources
Processing Resources (PRs) represent entities that are primarily algorithmic, such as parsers,
generators or ngram modellers.
They are created using the GATE Factory in manner similar the Language Resources. Besides the creation-time parameters they also have a set of run-time parameters that are set
by the system just before executing them.
Analysers are a particular type of processing resources in the sense that they always have a
document and a corpus among their run-time parameters.
The most used methods for Processing Resources are presented in table 7.7
7.6
Controllers
Controllers are used to create GATE applications. A Controller handles a set of Processing
Resources and can execute them following a particular strategy. GATE provides a series of
serial controllers (i.e. controllers that run their PRs in sequence):
GATE Embedded
147
Method
void setParameterValue(String
paramaterName, Object
parameterValue)
void setParameterValues(FeatureMap parameters)
Object getParameterValue(String
paramaterName)
Resource init()
void reInit()
void execute()
void interrupt()
boolean isInterrupted()
Purpose
Sets the value for a specified parameter.
method inherited from gate.Resource
Sets the values for more parameters
in one step. method inherited from
gate.Resource
Gets the value of a named parameter
of this resource. method inherited from
gate.Resource
Initialise this resource, and return it.
method inherited from gate.Resource
Reinitialises the processing resource.
After calling this method the resource
should be in the state it is after calling
init. If the resource depends on external
resources (such as rules files) then the
resource will re-read those resources. If
the data used to create the resource has
changed since the resource has been created then the resource will change too
after calling reInit().
Starts the execution of this Processing
Resource.
Notifies this PR that it should stop its
execution as soon as possible.
Checks whether this PR has been interrupted since the last time its Executable.execute() method was called.
Table 7.7: gate.ProcessingResource methods.
148
GATE Embedded
gate.creole.SerialController: a serial controller that takes any kind of PRs.
gate.creole.SerialAnalyserController: a serial controller that only accepts Language
Analysers as member PRs.
gate.creole.ConditionalSerialController: a serial controller that accepts all types of
PRs and that allows the inclusion or exclusion of member PRs from the execution
chain according to certain run-time conditions (currently features on the document
being processed are used).
gate.creole.ConditionalSerialAnalyserController: a serial controller that only accepts Language Analysers and that allows the conditional run of member PRs.
gate.creole.RealtimeCorpusController: a SerialAnalyserController that allows you
to specify a timeout parameter. If processing for a document takes longer than this
timeout then it will be forcibly terminated and the controller will move on to the next
document. Also if an exception occurs while processing a document this will simply
cause the controller to move on to the next document rather than failing the entire
corpus processing.
Additionally there is a scriptable controller provided by the Groovy plugin. See section 7.16.3
for details.
GATE Embedded
149
Creating an ANNIE application and running it over a corpus
1
2
3
// load the ANNIE plugin
Gate . ge tCreol eRegis ter (). reg i s te r D ir e c to r i es ( new File (
Gate . getPluginsHome () , " ANNIE " ). toURI (). toURL ());
4
5
6
7
8
9
10
// create a serial analyser controller to run ANNIE with
S e r i a l A n a l y s e r C o n t r o l l e r annieController =
( S e r i a l A n a l y s e r C o n t r o l l e r ) Factory . createResource (
" gate . creole . S e r i a l A n a l y s e r C o n t r o l l e r " ,
Factory . newFeatureMap () ,
Factory . newFeatureMap () , " ANNIE " );
11
12
13
14
15
16
17
18
19
20
21
// load each PR as defined in ANNIEConstants
for ( int i = 0; i < ANNIEConstants . PR_NAMES . length ; i ++) {
// use default parameters
FeatureMap params = Factory . newFeatureMap ();
P roc es si ng Re so ur ce pr = ( P ro ces si ng Re so ur ce )
Factory . createResource ( ANNIEConstants . PR_NAMES [ i ] ,
params );
// add the PR to the pipeline controller
annieController . add ( pr );
} // for each ANNIE PR
22
23
24
25
26
27
// Tell ANNIE’s controller about the corpus you want to run on
Corpus corpus = ...;
annieController . setCorpus ( corpus );
// Run ANNIE
annieController . execute ();
7.7
Duplicating a Resource
Sometimes, particularly in a multi-threaded application, it is useful to be able to create an
independent copy of an existing PR, controller or LR. The obvious way to do this is to call
createResource again, passing the same class name, parameters, features and name, and
for many resources this will do the right thing. However there are some resources for which
this may be insufficient (e.g. controllers, which also need to duplicate their PRs), unsafe
(if a PR uses temporary files, for instance), or simply inefficient. For example for a large
gazetteer this would involve loading a second copy of the lists into memory and compiling
them into a second identical state machine representation, but a much more efficient way to
achieve the same behaviour would be to use a SharedDefaultGazetteer (see section 13.11),
which can re-use the existing state machine.
The GATE Factory provides a duplicate method which takes an existing resource instance
and creates and returns an independent copy of the resource. By default it uses the algorithm
described above, extracting the parameter values from the template resource and calling
createResource to create a duplicate. However, if a particular resource type knows of a
better way to duplicate itself it can implement the CustomDuplication interface, and provide
its own duplicate method which the factory will use instead of performing the default
150
GATE Embedded
duplication algorithm. A caller who needs to duplicate an existing resource can simply call
Factory.duplicate to obtain a copy, which will be constructed in the appropriate way
depending on the resource type.
Note that the duplicate object returned by Factory.duplicate will not necessarily be of the
same class as the original object. However the contract of Factory.duplicate specifies that
where the original object implements any of a list of core GATE interfaces, the duplicate
can be assumed to implement the same ones – if you duplicate a DefaultGazetteer the
result may not be an instance of DefaultGazetteer but it is guaranteed to implement the
Gazetteer interface.
Full details of how to implement a custom duplicate method in your own resource type
can be found in the JavaDoc documentation for the CustomDuplication interface and the
Factory.duplicate method.
7.8
Persistent Applications
GATE Embedded allows the persistent storage of applications in a format based on XML
serialisation. This is particularly useful for applications management and distribution. A
developer can save the state of an application when he/she stops working on its design and
continue developing it in a next session. When the application reaches maturity it can be
deployed to the client site using the same method.
When an application (i.e. a Controller) is saved, GATE will actually only save the values for
the parameters used to create the Processing Resources that are contained in the application.
When the application is reloaded, all the PRs will be re-created using the saved parameters.
Many PRs use external resources (files) to define their behaviour and, in most cases, these
files are identified using URLs. During the saving process, all the URLs are converted relative
URLs based on the location of the application file. This way, if the resources are packaged
together with the application file, the entire application can be reliably moved to a different
location.
API access to application saving and loading is provided by means of two static methods on
the gate.util.persistence.PersistenceManager class, listed in table 7.8.
GATE Embedded
151
Method
public static void saveObjectToFile(Object obj, File file)
public static Object loadObjectFromFile(File file)
Purpose
Saves the data needed to re-create the
provided GATE object to the specified file. The Object provided can be
any type of Language or Processing Resource or a Controller. The procedures
may work for other types of objects as
well (e.g. it supports most Collection
types).
Parses the file specified (which needs to
be a file created by the above method)
and creates the necessary object(s) as
specified by the data in the file. Returns
the root of the object tree.
Table 7.8: Application Saving and Loading
Saving and loading a GATE application
¯
1
2
3
4
//Where to save the application?
File file = ...;
//What to save?
Controller theApplication = ...;
5
6
7
8
9
10
11
//save
gate . util . persistence . Per si st en ce Ma na ge r .
saveObjectToFile ( theApplication , file );
//delete the application
Factory . deleteResource ( theApplication );
theApplication = null ;
12
13
14
15
16
[...]
//load the application back
theApplication = gate . util . persistence . Per si st en ce Ma na ge r .
lo ad Ob je ct Fr om Fi le ( file );
7.9
Ontologies
Starting from GATE version 3.1, support for ontologies has been added. Ontologies are
nominally Language Resources but are quite different from documents and corpora and are
detailed in chapter 14.
Classes related to ontologies are to be found in the gate.creole.ontology package and its
sub-packages. The top level package defines an abstract API for working with ontologies
while the sub-packages contain concrete implementations. A client program should only use
the classes and methods defined in the API and never any of the classes or methods from
the implementation packages.
152
GATE Embedded
The entry point to the ontology API is the gate.creole.ontology.Ontology interface
which is the base interface for all concrete implementations. It provides methods for accessing
the class hierarchy, listing the instances and the properties.
Ontology implementations are available through plugins. Before an ontology language resource can be created using the gate.Factory and before any of the classes and methods in
the API can be used, one of the implementing ontology plugins must be loaded. For details
see chapter 14.
7.10
Creating a New Annotation Schema
An annotation schema (see Section 3.4.6) can be brought inside GATE through the creole.xml
file. By using the AUTOINSTANCE element, one can create instances of resources defined
in creole.xml. The gate.creole.AnnotationSchema (which is the Java representation of an
annotation schema file) initializes with some predefined annotation definitions (annotation
schemas) as specified by the GATE team.
Example from GATE’s internal creole.xml (in src/gate/resources/creole):
<!-- Annotation schema -->
<RESOURCE>
<NAME>Annotation schema</NAME>
<CLASS>gate.creole.AnnotationSchema</CLASS>
<COMMENT>An annotation type and its features</COMMENT>
<PARAMETER NAME="xmlFileUrl" COMMENT="The url to the definition file"
SUFFIXES="xml;xsd">java.net.URL</PARAMETER>
<AUTOINSTANCE>
<PARAM NAME ="xmlFileUrl" VALUE="schema/AddressSchema.xml" />
</AUTOINSTANCE>
<AUTOINSTANCE>
<PARAM NAME ="xmlFileUrl" VALUE="schema/DateSchema.xml" />
</AUTOINSTANCE>
<AUTOINSTANCE>
<PARAM NAME ="xmlFileUrl" VALUE="schema/FacilitySchema.xml" />
</AUTOINSTANCE>
<!-- etc. -->
</RESOURCE>
In order to create a gate.creole.AnnotationSchema object from a schema annotation file, one
must use the gate.Factory class;
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FeatureMap params = new FeatureMap ();\\
param . put ( " xmlFileUrl " , annotSchemaFile . toURL ());\\
AnnotationSchema annotSchema = \\
Factory . createResurce ( " gate . creole . AnnotationSchema " , params );
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Note: All the elements and their values must be written in lower case, as XML is defined as
case sensitive and the parser used for XML Schema inside GATE searches is case sensitive.
In order to be able to write XML Schema definitions, the ones defined in GATE
(resources/creole/schema) can be used as a model, or the user can have a look at
http://www.w3.org/2000/10/XMLSchema for a proper description of the semantics of the
elements used.
Some examples of annotation schemas are given in Section 5.4.1.
7.11
Creating a New CREOLE Resource
To create a new resource you need to:
• write a Java class that implements GATE’s beans model;
• compile the class, and any others that it uses, into a Java Archive (JAR) file;
• write some XML configuration data for the new resource;
• tell GATE the URL of the new JAR and XML files.
GATE Developer helps you with this process by creating a set of directories and files that
implement a basic resource, including a Java code file and a Makefile. This process is called
‘bootstrapping’.
For example, let’s create a new component called GoldFish, which will be a Processing
Resource that looks for all instances of the word ‘fish’ in a document and adds an annotation
of type ‘GoldFish’.
First start GATE Developer (see Section 2.2). From the ‘Tools’ menu select ‘BootStrap
Wizard’, which will pop up the dialogue in figure 7.2. The meaning of the data entry fields:
• The ‘resource name’ will be displayed when GATE Developer loads the resource, and
will be the name of the directory the resource lives in. For our example: GoldFish.
• ‘Resource package’ is the Java package that the class representing the resource will be
created in. For our example: sheffield.creole.example.
• ‘Resource type’ must be one of Language, Processing or Visual Resource. In this
case we’re going to process documents (and add annotations to them), so we select
ProcessingResource.
• ‘Implementing class name’ is the name of the Java class that represents the resource.
For our example: GoldFish.
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GATE Embedded
Figure 7.2: BootStrap Wizard Dialogue
• The ‘interfaces implemented’ field allows you to add other interfaces (e.g.
gate.creole.ControllerAwarePR4 ) that you would like your new resource to implement. In this case we just leave the default (which is to implement the
gate.ProcessingResource interface).
• The last field selects the directory that you want the new resource created in. For our
example: z:/tmp.
Now we need to compile the class and package it into a JAR file. The bootstrap wizard
creates an Ant build file that makes this very easy – so long as you have Ant set up properly,
you can simply run ant jar
This will compile the Java source code and package the resulting classes into GoldFish.jar.
If you don’t have your own copy of Ant, you can use the one bundled with GATE
- suppose your GATE is installed at /opt/gate-5.0-snapshot, then you can use
/opt/gate-5.0-snapshot/bin/ant jar to build.
You can now load this resource into GATE; see Section 3.6. The default Java code that was
created for our GoldFish resource looks like this:
/∗
∗
∗
∗
∗
∗
∗
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GoldFish . java
You s h o u l d p r o b a b l y p u t a c o p y r i g h t n o t i c e h e r e . Why n o t u s e t h e
GNU l i c e n c e ? ( S e e h t t p : / / www . gnu . o r g / . )
hamish , 2 6 / 9 / 2 0 0 1
See Section 4.4.
GATE Embedded
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∗
∗ $ I d : h o w t o . t e x , v 1 . 1 3 0 2 0 0 6 / 1 0 / 2 3 1 2 : 5 6 : 3 7 i a n Exp $
∗/
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package sheffield . creole . example ;
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import
import
import
import
java . util .*;
gate .*;
gate . creole .*;
gate . util .*;
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/∗ ∗
∗ T h i s c l a s s i s t h e i m p l e m e n t a t i o n o f t h e r e s o u r c e GOLDFISH .
∗/
@CreoleResource ( name = " GoldFish " ,
comment = " Add a descriptive comment about this resource " )
public class GoldFish extends A b s t r a c t P r o c e s s i n g R e s o u r c e
implements P roc es si ng Re so ur ce {
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} // class GoldFish
The default XML configuration for GoldFish looks like this:
<!-- creole.xml GoldFish -->
<!-- hamish, 26/9/2001 -->
<!-- $Id: howto.tex,v 1.130 2006/10/23 12:56:37 ian Exp $ -->
<CREOLE-DIRECTORY>
<JAR SCAN="true">GoldFish.jar</JAR>
</CREOLE-DIRECTORY>
The directory structure containing these files is shown in figure 7.3. GoldFish.java lives
in the src/sheffield/creole/example directory. creole.xml and build.xml are in the
top GoldFish directory. The lib directory is for libraries; the classes directory is where
Java class files are placed; the doc directory is for documentation. These last two, plus
GoldFish.jar are created by Ant.
This process has the advantage that it creates a complete source tree and build structure
for the component, and the disadvantage that it creates a complete source tree and build
structure for the component. If you already have a source tree, you will need to chop out the
bits you need from the new tree (in this case GoldFish.java and creole.xml) and copy it
into your existing one.
See the example code at http://gate.ac.uk/wiki/code-repository/.
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GATE Embedded
Figure 7.3: BootStrap directory tree
7.12
Adding Support for a New Document Format
In order to add a new document format, one needs to extend the gate.DocumentFormat
class and to implement an abstract method called:
1
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public void unpackMarkup ( Document doc ) throws
DocumentFormatException
This method is supposed to implement the functionality of each format reader and to create
annotations on the document. Finally the document’s old content will be replaced with a
new one containing only the text between markups.
If one needs to add a new textual reader will extend the gate.corpora.TextualDocumentFormat
and override the unpackMarkup(doc) method.
This class needs to be implemented under the Java bean specifications because it will be
instantiated by GATE using Factory.createResource() method.
The init() method that one needs to add and implement is very important because in here
the reader defines its means to be selected successfully by GATE. What one needs to do is
to add some specific information into certain static maps defined in DocumentFormat class,
that will be used at reader detection time.
After that, a definition of the reader will be placed into the one’s creole.xml file and the
reader will be available to GATE.
We present for the rest of the section a complete three step example of adding such a reader.
The reader we describe in here is an XML reader.
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157
Step 1
Create a new class called XmlDocumentFormat that extends
gate.corpora.TextualDocumentFormat.
Step 2
Implement the unpackMarkup(Document doc) which performs the required functionality for
the reader. Add XML detection means in init() method:
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public Resource init () throws R e s o u r c e I n s t a n t i a t i o n E x c e p t i o n {
// Register XML mime type
MimeType mime = new MimeType ( " text " ," xml " );
// Register the class handler for this mime type
m i m e S t r i n g 2 C l a s s H a n d l e r M a p . put ( mime . getType ()+ " / " + mime . getSubtype () ,
this );
// Register the mime type with mine string
m i m e S t r i n g 2 m i m e T y p e M a p . put ( mime . getType () + " / " + mime . getSubtype () ,
mime );
// Register file suffixes for this mime type
s u f f i x e s 2 m i m e T y p e M a p . put ( " xml " , mime );
s u f f i x e s 2 m i m e T y p e M a p . put ( " xhtm " , mime );
s u f f i x e s 2 m i m e T y p e M a p . put ( " xhtml " , mime );
// Register magic numbers for this mime type
mag ic2mim eTypeM ap . put ( " <? xml " , mime );
// Set the mimeType for this language resource
setMimeType ( mime );
return this ;
} // init()
More details about the information from those maps can be found in Section 5.5.1
Step 3
Add the following creole definition in the creole.xml document.
<RESOURCE>
<NAME>My XML Document Format</NAME>
<CLASS>mypackage.XmlDocumentFormat</CLASS>
<AUTOINSTANCE/>
<PRIVATE/>
</RESOURCE>
More information on the operation of GATE’s document format analysers may be found in
Section 5.5.
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7.13
Using GATE Embedded in a Multithreaded Environment
GATE Embedded can be used in multithreaded applications, so long as you observe a few
restrictions. First, you must initialise GATE by calling Gate.init() exactly once in your application, typically in the application startup phase before any concurrent processing threads
are started.
Secondly, you must not make calls that affect the global state of GATE (e.g. loading or
unloading plugins) in more than one thread at a time. Again, you would typically load all
the plugins your application requires at initialisation time. It is safe to create instances of
resources in multiple threads concurrently.
Thirdly, it is important to note that individual GATE processing resources, language resources and controllers are by design not thread safe – it is not possible to use a single
instance of a controller/PR/LR in multiple threads at the same time – but for a well written
resource it should be possible to use several different instances of the same resource at once,
each in a different thread. When writing your own resource classes you should bear the
following in mind, to ensure that your resource will be useable in this way.
• Avoid static data. Where possible, you should avoid using static fields in your class,
and you should try and take all configuration data via the CREOLE parameters you
declare in your creole.xml file. System properties may be appropriate for truly static
configuration, such as the location of an external executable, but even then it is generally better to stick to CREOLE parameters – a user may wish to use two different
instances of your PR, each talking to a different executable.
• Read parameters at the correct time. Init-time parameters should be read in the init()
(and reInit()) method, and for processing resources runtime parameters should be
read at each execute().
• Use temporary files correctly. If your resource makes use of external temporary files
you should create them using File.createTempFile() at init or execute time, as
appropriate. Do not use hardcoded file names for temporary files.
• If there are objects that can be shared between different instances of your resource,
make sure these objects are accessed either read-only, or in a thread-safe way. In
particular you must be very careful if your resource can take other resource instances
as init or runtime parameters (e.g. the Flexible Gazetteer, Section 13.7).
Of course, if you are writing a PR that is simply a wrapper around an external library that
imposes these kinds of limitations there is only so much you can do. If your resource cannot
be made safe you should document this fact clearly.
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159
All the standard ANNIE PRs are safe when independent instances are used in different
threads concurrently, as are the standard transient document, transient corpus and controller
classes. A typical pattern of development for a multithreaded GATE-based application is:
• Develop your GATE processing pipeline in GATE Developer.
• Save your pipeline as a .gapp file.
• In your application’s initialisation phase, load n copies of the pipeline using
PersistenceManager.loadObjectFromFile() (see the Javadoc documentation for details), or load the pipeline once and then make copies of it using Factory.duplicate
as described in section 7.7, and either give one copy to each thread or store them in a
pool (e.g. a LinkedList).
• When you need to process a text, get one copy of the pipeline from the pool, and
return it to the pool when you have finished processing.
Alternatively you can use the Spring Framework as described in the next section to handle
the pooling for you.
7.14
Using GATE Embedded within a Spring Application
GATE Embedded provides helper classes to allow GATE resources to be created and managed by the Spring framework. For Spring 2.0 or later, GATE Embedded provides a custom
namespace handler that makes them extremely easy to use. To use this namespace, put the
following declarations in your bean definition file:
<beans xmlns="http://www.springframework.org/schema/beans"
xmlns:gate="http://gate.ac.uk/ns/spring"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="
http://www.springframework.org/schema/beans
http://www.springframework.org/schema/beans/spring-beans.xsd
http://gate.ac.uk/ns/spring
http://gate.ac.uk/ns/spring.xsd">
You can have Spring initialise GATE:
<gate:init gate-home="WEB-INF" user-config-file="WEB-INF/user.xml">
<gate:preload-plugins>
<value>WEB-INF/ANNIE</value>
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GATE Embedded
<value>http://example.org/gate-plugin</value>
</gate:preload-plugins>
</gate:init>
The gate-home, user-config-file, etc. and the <value> elements under <gate:preload-plugins>
are interpreted as Spring “resource” paths. If the value is not an absolute URL then Spring
will resolve the path in an appropriate way for the type of application context — in a web
application they are taken as being relative to the web app root, and you would typically
use locations within WEB-INF as shown in the example above. To use an absolute path
for gate-home it is not sufficient to use a leading slash (e.g. /opt/gate), for backwardscompatibility reasons Spring will still resolve this relative to your web application. Instead
you must specify it as a full URL, i.e. file:/opt/gate.
<gate:preload-plugins> specifies CREOLE plugins that should be loaded after GATE
has been initialised. An alternative way to specify extra plugins is to provide separate
<gate:extra-plugin> elements, for example:
<gate:init gate-home="WEB-INF"
user-config-file="WEB-INF/user.xml" />
<gate:extra-plugin>WEB-INF/ANNIE</gate:extra-plugin>
You can freely mix the two styles – nested <gate:preload-plugins> definitions are processed first, followed by all the <gate:extra-plugin> definitions found in the application
context. This is useful if, for example, you are providing additional configuration as a separate bean definition file from the one containing the main <gate:init> definition and need
to load extra plugins without editing this main definition.
To create a GATE resource, use the <gate:resource> element.
<gate:resource id="sharedOntology" scope="singleton"
resource-class="gate.creole.ontology.owlim.OWLIMOntologyLR">
<gate:parameters>
<entry key="rdfXmlURL">
<gate:url>WEB-INF/ontology.rdf</gate:url>
</entry>
</gate:parameters>
<gate:features>
<entry key="ontologyVersion" value="0.1.3" />
<entry key="mainOntology">
<value type="java.lang.Boolean">true</value>
</entry>
</gate:features>
</gate:resource>
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161
The children of <gate:parameters> are Spring <entry/> elements, just as you would write
when configuring a bean property of type Map<String,Object>. <gate:url> provides a
way to construct a java.net.URL from a resource path as discussed above. If it is possible
to resolve the resource path as a file: URL then this form will be preferred, as there are a
number of areas within GATE which work better with file: URLs than with other types
of URL (for example plugins that run external processes, or that use a URL parameter to
point to a directory in which they will create new files).
A note about types: The <gate:parameters> and <gate:features> elements define GATE
FeatureMaps. When using the simple <entry key="..." value="..." /> form, the entry
values will be treated as strings; Spring can convert strings into many other types of object
using the standard Java Beans property editor mechanism, but since a FeatureMap can hold
any kind of values you must use an explicit <value type="...">...</value> to tell Spring
what type the value should be.
There is an additional twist for <gate:parameters> – GATE has its own internal logic
to convert strings to other types required for resource parameters (see the discussion of
default parameter values in section 4.7.1). So for parameter values you have a choice, you
can either use an explicit <value type="..."> to make Spring do the conversion, or you
can pass the parameter value as a string and let GATE do the conversion. For resource
parameters whose type is java.net.URL, if you pass a string value that is not an absolute
URL (starting file:, http:, etc.) then GATE will treat the string as a path relative to
the creole.xml file of the plugin that defines the resource type whose parameter you are
setting. If this is not what you intended then you should use <gate:url> to cause Spring to
resolve the path to a URL before passing it to GATE. For example, for a JAPE transducer,
<entry key="grammarURL" value="grammars/main.jape" /> would resolve to something
like file:/path/to/webapp/WEB-INF/plugins/ANNIE/grammars/main.jape, whereas
<entry key="grammarURL">
<gate:url>grammars/main.jape</gate:url>
</entry>
would resolve to file:/path/to/webapp/grammars/main.jape.
You can load a GATE saved application with
<gate:saved-application location="WEB-INF/application.gapp" scope="prototype">
<gate:customisers>
<gate:set-parameter pr-name="custom transducer" name="ontology"
ref="sharedOntology" />
</gate:customisers>
</gate:saved-application>
‘Customisers’ are used to customise the application after it is loaded. In the example above,
we load a singleton copy of an ontology which is then shared between all the separate
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instances of the (prototype) application. The <gate:set-parameter> customiser accepts
all the same ways to provide a value as the standard Spring <property> element (a ”value”
or ”ref” attribute, or a sub-element - <value>, <list>, <bean>, <gate:resource> . . . ).
The <gate:add-pr> customiser provides support for the case where most of the application
is in a saved state, but we want to create one or two extra PRs with Spring (maybe to inject
other Spring beans as init parameters) and add them to the pipeline.
<gate:saved-application ...>
<gate:customisers>
<gate:add-pr add-before="OrthoMatcher" ref="myPr" />
</gate:customisers>
</gate:saved-application>
By default, the <gate:add-pr> customiser adds the target PR at the end of the pipeline,
but an add-before or add-after attribute can be used to specify the name of a PR before
(or after) which this PR should be placed. Alternatively, an index attribute places the PR
at a specific (0-based) index into the pipeline. The PR to add can be specified either as a
‘ref’ attribute, or with a nested <bean> or <gate:resource> element.
7.14.1
Duplication in Spring
The above example defines the <gate:application> as a prototype-scoped bean, which
means the saved application state will be loaded afresh each time the bean is fetched from the
bean factory (either explicitly using getBean or implicitly when it is injected as a dependency
of another bean). However in many cases it is better to load the application once and then
duplicate it as required (as described in section 7.7), as this allows resources to optimise
their memory usage, for example by sharing a single in-memory representation of a large
gazetteer list between several instances of the gazetteer PR. This approach is supported by
the <gate:duplicate> tag.
<gate:duplicate id="theApp">
<gate:saved-application location="/WEB-INF/application.xgapp" />
</gate:duplicate>
The <gate:duplicate> tag acts like a prototype bean definition, in that each time it is
fetched or injected it will call Factory.duplicate to create a new duplicate of its template
resource (declared as a nested element or referenced by the template-ref attribute). However the tag also keeps track of all the duplicate instances it has returned over its lifetime,
and will ensure they are released (using Factory.deleteResource) when the Spring context
is shut down.
The <gate:duplicate> tag also supports customisers, which will be applied to the newlycreated duplicate resource before it is returned. This is subtly different from applying the
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customisers to the template resource itself, which would cause them to be applied once to
the original resource before it is first duplicated.
Finally, <gate:duplicate> takes an optional boolean attribute return-template. If set to
false (or omitted, as this is the default behaviour), the tag always returns a duplicate — the
original template resource is used only as a template and is not made available for use. If set
to true, the first time the bean defined by the tag is injected or fetched, the original template
resource is returned. Subsequent uses of the tag will return duplicates. Generally speaking,
it is only safe to set return-template="true" when there are no customisers, and when
the duplicates will all be created up-front before any of them are used. If the duplicates will
be created asynchronously (e.g. with a dynamically expanding pool, see below) then it is
possible that, for example, a template application may be duplicated in one thread whilst it
is being executed by another thread, which may lead to unpredictable behaviour.
7.14.2
Spring pooling
In a multithreaded application it is vital that individual GATE resources are not used in
more than one thread at the same time. Because of this, multithreaded applications that use
GATE Embedded often need to use some form of pooling to provided thread-safe access to
GATE components. This can be managed by hand, but the Spring framework has built-in
tools to support transparent pooling of Spring-managed beans. Spring can create a pool of
identical objects, then expose a single “proxy” object (offering the same interface) for use
by clients. Each method call on the proxy object will be routed to an available member of
the pool in such a way as to guarantee that each member of the pool is accessed by no more
than one thread at a time.
Since the pooling is handled at the level of method calls, this approach is not used to create a
pool of GATE resources directly — making use of a GATE PR typically involves a sequence
of method calls (at least setDocument(doc), execute() and setDocument(null)), and creating a pooling proxy for the resource may result in these calls going to different members
of the pool. Instead the typical use of this technique is to define a helper object with a single method that internally calls the GATE API methods in the correct sequence, and then
create a pool of these helpers. The interface gate.util.DocumentProcessor and its associated implementation gate.util.LanguageAnalyserDocumentProcessor are useful for this.
The DocumentProcessor interface defines a processDocument method that takes a GATE
document and performs some processing on it. LanguageAnalyserDocumentProcessor implements this interface using a GATE LanguageAnalyser (such as a saved “corpus pipeline”
application) to do the processing. A pool of LanguageAnalyserDocumentProcessor instances can be exposed through a proxy which can then be called from several threads.
The machinery to implement this is all built into Spring, but the configuration typically
required to enable it is quite fiddly, involving at least three co-operating bean definitions.
Since the technique is so useful with GATE Embedded, GATE provides a special syntax to
configure pooling in a simple way. Given the <gate:duplicate id="theApp"> definition
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from the previous section we can create a DocumentProcessor proxy that can handle up to
five concurrent requests as follows:
<bean id="processor"
class="gate.util.LanguageAnalyserDocumentProcessor">
<property name="analyser" ref="theApp" />
<gate:pooled-proxy max-size="5" />
</bean>
The <gate:pooled-proxy> element decorates a singleton bean definition. It converts the
original definition to prototype scope and replaces it with a singleton proxy delegating to a
pool of instances of the prototype bean. The pool parameters are controlled by attributes
of the <gate:pooled-proxy> element, the most important ones being:
max-size The maximum size of the pool. If more than this number of threads try to call
methods on the proxy at the same time, the others will (by default) block until an
object is returned to the pool.
initial-size The default behaviour of Spring’s pooling tools is to create instances in the
pool on demand (up to the max-size). This attribute instead causes initial-size
instances to be created up-front and added to the pool when it is first created.
when-exhausted-action-name What to do when the pool is exhausted (i.e. there are
already max-size concurrent calls in progress and another one arrives). Should be set
to one of WHEN_EXHAUSTED_BLOCK (the default, meaning block the excess requests until
an object becomes free), WHEN_EXHAUSTED_GROW (create a new object anyway, even
though this pushes the pool beyond max-size) or WHEN_EXHAUSTED_FAIL (cause the
excess calls to fail with an exception).
Many more options are available, corresponding to the properties of the Spring CommonsPoolTargetSource class. These allow you, for example, to configure a pool that dynamically
grows and shrinks as necessary, releasing objects that have been idle for a set amount of time.
See the JavaDoc documentation of CommonsPoolTargetSource (and the documentation for
Apache commons-pool) for full details.
Note that the <gate:pooled-proxy> technique is not tied to GATE in any way, it is simply
an easy way to configure standard Spring beans and can be used with any bean that needs
to be pooled, not just objects that make use of GATE.
7.14.3
Further reading
These custom elements all define various factory beans. For full details, see the JavaDocs for
gate.util.spring (the factory beans) and gate.util.spring.xml (the gate: namespace
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handler). The main Spring framework API documentation is the best place to look for more
detail on the pooling facilities provided by Spring AOP.
Note: the former approach using factory methods of the gate.util.spring.SpringFactory
class will still work, but should be considered deprecated in favour of the new factory beans.
7.15
Using GATE Embedded within a Tomcat Web
Application
Embedding GATE in a Tomcat web application involves several steps.
1. Put the necessary JAR files (gate.jar and all or most of the jars in gate/lib) in your
webapp/WEB-INF/lib.
2. Put the plugins that your application depends on in a suitable location (e.g.
webapp/WEB-INF/plugins).
3. Create suitable gate.xml configuration files for your environment.
4. Set the appropriate paths in your application before calling Gate.init().
This process is detailed in the following sections.
7.15.1
Recommended Directory Structure
You will need to create a number of other files in your web application to allow GATE to
work:
• Site and user gate.xml config files - we highly recommend defining these specifically for
the web application, rather than relying on the default files on your application server.
• The plugins your application requires.
In this guide, we assume the following layout:
webapp/
WEB-INF/
gate.xml
user-gate.xml
plugins/
ANNIE/
etc.
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7.15.2
Configuration Files
Your gate.xml (the ‘site-wide configuration file’) should be as simple as possible:
<?xml version="1.0" encoding="UTF-8" ?>
<GATE>
<GATECONFIG Save_options_on_exit="false"
Save_session_on_exit="false" />
</GATE>
Similarly, keep the user-gate.xml (the ‘user config file’) simple:
<?xml version="1.0" encoding="UTF-8" ?>
<GATE>
<GATECONFIG Known_plugin_path=";"
Load_plugin_path=";" />
</GATE>
This way, you can control exactly which plugins are loaded in your webapp code.
7.15.3
Initialization Code
Given the directory structure shown above, you can initialize GATE in your web application
like this:
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// imports
...
public class MyServlet extends HttpServlet {
private static boolean gateInited = false ;
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public void init () throws ServletException {
if (! gateInited ) {
try {
ServletContext ctx = get Servl etCont ext ();
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// use /path/to/your/webapp/WEB-INF as gate.home
File gateHome = new File ( ctx . getRealPath ( " / WEB - INF " ));
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Gate . setGateHome ( gateHome );
// thus webapp/WEB-INF/plugins is the plugins directory, and
// webapp/WEB-INF/gate.xml is the site config file.
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// Use webapp/WEB-INF/user-gate.xml as the user config file,
// to avoid confusion with your own user config.
Gate . se tUserC onfigF ile ( new File ( gateHome , " user - gate . xml " ));
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Gate . init ();
// load plugins, for example...
Gate . get Creol eRegis ter (). r eg i s te r D ir e c to r i es (
ctx . getResource ( " / WEB - INF / plugins / ANNIE " ));
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gateInited = true ;
}
catch ( Exception ex ) {
throw new ServletException ( " Exception initialising GATE " ,
ex );
}
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}
33
}
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}
Once initialized, you can create GATE resources using the Factory in the usual way (for
example, see Section 7.1 for an example of how to create an ANNIE application). You should
also read Section 7.13 for important notes on using GATE Embedded in a multithreaded
application.
Instead of an initialization servlet you could also consider doing your initialization in a
ServletContextListener, or using Spring (see Section 7.14).
7.16
Groovy for GATE
Groovy is a dynamic programming language based on Java. Groovy is not used in the
core GATE distribution, so to enable the Groovy features in GATE you must first load the
Groovy plugin. Loading this plugin:
• provides access to the Groovy scripting console (configured with some extensions for
GATE) from the GATE Developer “Tools” menu.
• provides a PR to run a Groovy script over documents.
• provides a controller which uses a Groovy DSL to define its execution strategy.
• enhances a number of core GATE classes with additional convenience methods that can
be used from any Groovy code including the console, the script PR, and any Groovy
class that uses the GATE Embedded API.
This section describes these features in detail, but assumes that the reader already
has some knowledge of the Groovy language. If you are not already familiar with
Groovy you should read this section in conjunction with Groovy’s own documentation at
http://groovy.codehaus.org/.
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7.16.1
Groovy Scripting Console for GATE
Loading the Groovy plugin in GATE Developer will provide a “Groovy Console” item in
the Tools/Groovy Tools menu. This menu item opens the standard Groovy console window
(http://groovy.codehaus.org/Groovy+Console).
To help scripting GATE in Groovy, the console is pre-configured to import all classes from
the gate, gate.annotation, gate.util, gate.jape and gate.creole.ontology packages
of the core GATE API5 . This means you can refer to classes and interfaces such as Factory,
AnnotationSet, Gate, etc. without needing to prefix them with a package name. In addition,
the following (read-only) variable bindings are pre-defined in the Groovy Console.
• corpora: a list of loaded corpora LRs (Corpus)
• docs: a list of all loaded document LRs (DocumentImpl)
• prs: a list of all loaded PRs
• apps: a list of all loaded Applications (AbstractController)
These variables are automatically updated as resources are created and deleted in GATE.
Here’s an example script. It finds all documents with a feature “annotator” set to “fred”,
and puts them in a new corpus called “fredsDocs”.
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Factory . newCorpus ( " fredsDocs " ). addAll (
docs . findAll {
it . features . annotator == " fred "
}
)
You can find other examples (and add your own) in the Groovy script repository on the
GATE Wiki: http://gate.ac.uk/wiki/groovy-recipes/.
Why won’t the ‘Groovy executing’ dialog go away? Sometimes, when you execute a
Groovy script through the console, a dialog will appear, saying “Groovy is executing. Please
wait”. The dialog fails to go away even when the script has ended, and cannot be closed by
clicking the “Interrupt” button. You can, however, continue to use the Groovy Console, and
the dialog will usually go away next time you run a script. This is not a GATE problem: it
is a Groovy problem.
5
These are the same classes that are imported by default for use in Java code on the right hand side of
JAPE rules.
GATE Embedded
7.16.2
169
Groovy scripting PR
The Groovy scripting PR enables you to load and execute Groovy scripts as part of a GATE
application pipeline. The Groovy scripting PR is made available when you load the Groovy
plugin via the plugin manager.
Parameters
The Groovy scripting PR has a single initialisation parameter
• scriptURL: the path to a valid Groovy script
It has three runtime parameters
• inputASName: an optional annotation set intended to be used as input by the PR
(but note that the PR has access to all annotation sets)
• outputASName: an optional annotation set intended to be used as output by the
PR (but note that the PR has access to all annotation sets)
• scriptParams: optional parameters for the script. In a creole.xml file, these should
be specified as key=value pairs, each pair separated by a comma. For example:
’name=fred,type=person’ . In the GATE GUI, these are specified via a dialog.
Script bindings
As with the Groovy console described above, and with JAPE right-hand-side Java code,
Groovy scripts run by the scripting PR implicitly import all classes from the gate,
gate.annotation, gate.util, gate.jape and gate.creole.ontology packages of the core
GATE API. The Groovy scripting PR also makes available the following bindings, which you
can use in your scripts:
• doc: the current document (Document)
• corpus: the corpus containing the current document
• content: the string content of the current document
• inputAS: the annotation set specified by inputASName in the PRs runtime parameters
• outputAS: the annotation set specified by outputASName in the PRs runtime parameters
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Note that inputAS and outputAS are intended to be used as input and output AnnotationSets. This is, however, a convention: there is nothing to stop a script writing to or reading
from any AnnotationSet. Also, although the script has access to the corpus containing the
document it is running over, it is not generally necessary for the script to iterate over the
documents in the corpus itself – the reference is provided to allow the script to access data
stored in the FeatureMap of the corpus. Any other variables assigned to within the script
code will be added to the binding, and values set while processing one document can be used
while processing a later one.
Passing parameters to the script
In addition to the above bindings, one further binding is available to the script:
• scriptParams: a FeatureMap with keys and values as specified by the scriptParams
runtime parameter
For example, if you were to create a scriptParams runtime parameter for your PR, with the
keys and values: ’name=fred,type=person’, then the values could be retrieved in your script
via scriptParams.name and scriptParams.type
Controller callbacks
A Groovy script may wish to do some pre- or post-processing before or after processing
the documents in a corpus, for example if it is collecting statistics about the corpus. To
support this, the script can declare methods beforeCorpus and afterCorpus, taking a
single parameter. If the beforeCorpus method is defined and the script PR is running in
a corpus pipeline application, the method will be called before the pipeline processes the
first document. Similarly, if the afterCorpus method is defined it will be called after the
pipeline has completed processing of all the documents in the corpus. In both cases the
corpus will be passed to the method as a parameter. If the pipeline aborts with an exception
the afterCorpus method will not be called, but if the script declares a method aborted(c)
then this will be called instead.
Note that because the script is not processing a particular document when these methods
are called, the usual doc, corpus, inputAS, etc. are not available within the body of the
methods (though the corpus is passed to the method as a parameter). The scriptParams
variable is available.
The following example shows how this technique could be used to build a simple tf/idf
index for a GATE corpus. The example is available in the GATE distribution as
plugins/Groovy/resources/scripts/tfidf.groovy. The script makes use of some of the
utility methods described in section 7.16.4.
GATE Embedded
1
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3
4
5
6
7
8
9
// reset variables
void beforeCorpus ( c ) {
// list of maps (one for each doc) from term to frequency
frequencies = []
// sorted map from term to docs that contain it
docMap = new TreeMap ()
// index of the current doc in the corpus
docNum = 0
}
10
11
12
// start frequency list for this document
frequencies << [:]
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16
17
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19
// iterate over the requested annotations
inputAS [ scriptParams . annotationType ]. each {
def str = doc . stringFor ( it )
// increment term frequency for this term
frequencies [ docNum ][ str ] =
( frequencies [ docNum ][ str ] ?: 0) + 1
20
// keep track of which documents this term appears in
if (! docMap [ str ]) {
docMap [ str ] = new LinkedHashSet ()
}
docMap [ str ] << docNum
21
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26
}
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32
// normalize counts by doc length
def docLength = inputAS [ scriptParams . annotationType ]. size ()
frequencies [ docNum ]. each { freq ->
freq . value = (( double ) freq . value ) / docLength
}
33
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// increment the counter for the next document
docNum ++
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43
44
45
46
47
48
// compute the IDFs and store the table as a corpus feature
void afterCorpus ( c ) {
def tfIdf = [:]
docMap . each { term , docsWithTerm ->
def idf = Math . log (( double ) docNum / docsWithTerm . size ())
tfIdf [ term ] = [:]
docsWithTerm . each { docId ->
tfIdf [ term ][ docId ] = frequencies [ docId ][ term ] * idf
}
}
c . features . freqTable = tfIdf
}
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Examples
The plugin directory Groovy/resources/scripts contains some example scripts. Below is the
code for a naive regular expression PR.
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3
4
5
6
7
matcher = content =~ scriptParams . regex
while ( matcher . find ())
outputAS . add ( matcher . start () ,
matcher . end () ,
scriptParams . type ,
Factory . newFeatureMap ())
The script needs to have the runtime parameter scriptParams set with keys and values as
follows:
• regex: the Groovy regular expression that you want to match e.g. [^\s]*ing
• type: the type of the annotation to create for each regex match, e.g. regexMatch
When the PR is run over a document, the script will first make a matcher over the document
content for the regular expression given by the regex parameter. It will iterate over all
matches for this regular expression, adding a new annotation for each, with a type as given
by the type parameter.
7.16.3
The Scriptable Controller
The Groovy plugin’s “Scriptable Controller” is a more flexible alternative to the standard
pipeline (SerialController) and corpus pipeline (SerialAnalyserController) applications and their conditional variants, and also supports the time limiting and robustness
features of the realtime controller. Like the standard controllers, a scriptable controller contains a list of processing resources and can optionally be configured with a corpus, but unlike
the standard controllers it does not necessarily execute the PRs in a linear order. Instead
the execution strategy is controlled by a script written in a Groovy domain specific language
(DSL), which is detailed in the following sections.
Running a single PR
To run a single PR from the scriptable controller’s list of PRs, simply use the PR’s name as
a Groovy method call:
1
2
somePr ()
" ANNIE English Tokeniser " ()
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173
If the PR’s name contains spaces or any other character that is not valid in a Groovy
identifier, or if the name is a reserved word (such as “import”) then you must enclose the
name in single or double quotes. You may prefer to rename the PRs so their names are valid
identifiers. Also, if there are several PRs in the controller’s list with the same name, they
will all be run in the order in which they appear in the list.
You can optionally provide a Map of named parameters to the call, and these will override
the corresponding runtime parameter values for the PR (the original values will be restored
after the PR has been executed):
1
myTransducer ( outputASName : " output " )
Iterating over the corpus
If a corpus has been provided to the controller then you can iterate over all the documents
in the corpus using eachDocument:
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2
3
4
5
eachDocument {
tokeniser ()
sentenceSplitter ()
myTransducer ()
}
The block of code (in fact a Groovy closure) is executed once for each document in the corpus
exactly as a standard corpus pipeline application would operate. The current document is
available to the script in the variable doc and the corpus in the variable corpus, and in
addition any calls to PRs that implement the LanguageAnalyser interface will set the PR’s
document and corpus parameters appropriately.
Running all the PRs in sequence
Calling allPRs() will execute all the controller’s PRs once in the order in which they appear
in the list. This is rarely useful in practice but it serves to define the default behaviour:
the initial script that is used by default in a newly instantiated scriptable controller is
eachDocument { allPRs() }, which mimics the behaviour of a standard corpus pipeline application.
More advanced scripting
The basic DSL is extremely simple, but because the script is Groovy code you can use all
the other facilities of the Groovy language to do conditional execution, grouping of PRs,
etc. The control script has the same implicit imports as provided by the Groovy Script PR
(section 7.16.2), and additional import statements can be added as required.
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For example, suppose you have a pipeline for multi-lingual document processing, containing
PRs named “englishTokeniser”, “englishGazetteer”, “frenchTokeniser”, “frenchGazetteer”,
“genericTokeniser”, etc., and you need to choose which ones to run based on a document
feature:
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3
4
5
eachDocument {
def lang = doc . features . language ?: ’ generic ’
" $ { lang } Tokeniser " ()
" $ { lang } Gazetteer " ()
}
As another example, suppose you have a particular JAPE grammar that you know is slow
on documents that mention a large number of locations, so you only want to run it on
documents with up to 100 Location annotations, and use a faster but less accurate one on
others:
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3
4
5
6
7
// helper method to group several PRs together
void an notate Locati ons () {
tokeniser ()
splitter ()
gazetteer ()
locationGrammar ()
}
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15
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eachDocument {
ann otateL ocatio ns ()
if ( doc . annotations [ " Location " ]. size () <= 100) {
f u l l L o c a t i o n C l a s s i f i e r ()
}
else {
f a s t L o c a t i o n C l a s s i f i e r ()
}
}
You can have more than one call to eachDocument, for example a controller that pre-processes
some documents, then collects some corpus-level statistics, then further processes the documents based on those statistics.
As a final example, consider a controller to post-process data from a manual annotation
task. Some of the documents have been annotated by one annotator, some by more than
one (the annotations are in sets named “annotator1”, “annotator2”, etc., but the number of
sets varies from document to document).
1
2
3
4
5
6
eachDocument {
// find all the annotatorN sets on this document
def annotators =
doc . an no ta ti onS et Na me s . findAll {
it ==~ / annotator \ d +/
}
7
8
9
// run the post-processing JAPE grammar on each one
annotators . each { asName ->
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175
postProcessingGrammar (
inputASName : asName ,
outputASName : asName )
10
11
12
}
13
14
// now merge them to form a consensus set
mergingPR ( annSe tsForM erging : annotators . join ( ’; ’ ))
15
16
}
17
Nesting a scriptable controller in another application
Like the standard SerialAnalyserController, the scriptable controller implements the
LanugageAnalyser interface and so can itself be nested as a PR in another pipeline. When
used in this way, eachDocument does not iterate over the corpus but simply calls its closure
once, with the “current document” set to the document that was passed to the controller as
a parameter. This is the same logic as is used by SerialAnalyserController, which runs its
PRs once only rather than once per document in the corpus.
Ignoring errors
By default, if an exception or error occurs while processing (either thrown by a PR or
occurring directly within the controller’s script) then the controller’s execution will terminate
with an exception. If this occurs during an eachDocument then the remaining documents
will not be processed. In some circumstances it may be preferable to ignore the error and
simply continue with the next document. To support this you can use ignoringErrors:
eachDocument {
ignoringErrors {
tokeniser ()
sentenceSplitter ()
myTransducer ()
}
}
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2
3
4
5
6
7
Any exceptions or errors thrown within the ignoringErrors block will be logged6 but not
rethrown. So in the example above if myTransducer fails with an exception the controller
will continue with the next document. Note that it is important to nest the blocks correctly
– if the nesting were reversed (with the eachDocument inside the ignoringErrors) then
an exception would terminate the whole eachDocument loop and the remaining documents
would not be processed.
6
to the gate.groovy.ScriptableController Log4J logger
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GATE Embedded
Realtime behaviour
Some GATE processing resources can be very slow when operating on large or complex
documents. In many cases it is possible to use heuristics within your controller’s script to
spot likely “problem” documents and avoid running such PRs over them (see the fast vs.
full location classifier example above), but for situations where this is not possible you can
use the timeLimit method to put a blanket limit on the time that PRs will be allowed to
consume, in a similar way to the real-time controller.
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2
3
4
5
6
7
8
eachDocument {
ignoringErrors {
ann otateL ocatio ns ()
timeLimit ( soft :30. seconds , hard :30. seconds ) {
cla ssifyL ocatio ns ()
}
}
}
A call to timeLimit will attempt to limit the running time of its associated code block. You
can specify three different kinds of limit:
soft if the block is still executing after this time, attempt to interrupt it gently. This
uses Thread.interrupt() and also calls the interrupt() method of the currently
executing PR (if any).
exception if the block is still executing after this time beyond the soft limit, attempt to
induce an exception by setting the corpus and document parameters of the currently
running PR to null. This is useful to deal with PRs that do not properly respect the
interrupt call.
hard if the block is still executing after this time beyond the previous limit, forcibly terminate it using Thread.stop. This is inherently dangerous and prone to memory leakage
but may be the only way to stop particularly stubborn PRs. It should be used with
caution.
Limits can be specified using Groovy’s TimeCategory notation as shown above (e.g.
10.seconds, 2.minutes, 1.minute+45.seconds), or as simple numbers (of milliseconds).
Each limit starts counting from the end of the last, so in the example above the hard limit
is 30 seconds after the soft limit, or 1 minute after the start of execution. If no hard limit is
specified the controller will wait indefinitely for the block to complete.
Note also that when a timeLimit block is terminated it will throw an exception. If you do
not wish this exception to terminate the execution of the controller as a whole you will need
to wrap the timeLimit block in an ignoringErrors block.
timeLimit blocks, particularly ones with a hard limit specified, should be regarded as a last
resort – if there are heuristic methods you can use to avoid running slow PRs in the first place
GATE Embedded
177
Figure 7.4: Accessing the script editor for a scriptable controller
it is a good idea to use them as a first defence, possibly wrapping them in a timeLimit block
if you need hard guarantees (for example when you are paying per hour for your compute
time in a cloud computing system).
The Scriptable Controller in GATE Developer
When you double-click on a scriptable controller in the resources tree of GATE Developer
you see the same controller editor that is used by the standard controllers. This view allows
you to add PRs to the controller and set their default runtime parameter values, and to
specify the corpus over which the controller should run. A separate view is provided to
allow you to edit the Groovy script, which is accessible via the “Control Script” tab (see
figure 7.4). This tab provides a text editor which does basic Groovy syntax highlighting (the
same editor used by the Groovy Console).
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7.16.4
Utility methods
Loading the Groovy plugin adds some additional methods to several of the core GATE API
classes and interfaces using the Groovy “mixin” mechanism. Any Groovy code that runs
after the plugin has been loaded can make use of these additional methods, including snippets
run in the Groovy console, scripts run using the Script PR, and any other Groovy code that
uses the GATE Embedded API.
The methods that are injected come from two classes. The gate.Utils class (part of the core
GATE API in gate.jar) defines a number of static methods that can be used to simplify
common tasks such as getting the string covered by an annotation or annotation set, finding
the start or end offset of an annotation (or set), etc. These methods do not use any Groovyspecific types, so they are usable from pure Java code in the usual way as well as being mixed
in for use in Groovy. Additionally, the class gate.groovy.GateGroovyMethods (part of the
Groovy plugin) provides methods that use Groovy types such as closures and ranges.
The added methods include:
• Unified access to the start and end offsets of an Annotation, AnnotationSet or
Document: e.g. someAnnotation.start() or anAnnotationSet.end()
• Simple access to the DocumentContent or string covered by an annotation or annotation set: document.stringFor(anAnnotation), document.contentFor(annotationSet)
• Simple access to the length of an annotation or document,
(annotation.length()) or a long (annotation.lengthLong()).
either as an int
• A method to construct a FeatureMap from any map, to support constructions like def
params = [sourceUrl:’http://gate.ac.uk’, encoding:’UTF-8’].toFeatureMap()
• A method to convert an annotation set into a List of annotations in the order they appear
in the document, for iteration in a predictable order: annSet.inDocumentOrder().collect
{ it.type }
• The each, eachWithIndex and collect methods for a corpus have been redefined to properly
load and unload documents if the corpus is stored in a datastore.
• Various getAt methods to support constructions like annotationSet["Token"] (get all Token annotations from the set), annotationSet[15..20] (get all annotations between offsets
15 and 20), documentContent[0..10] (get the document content between offsets 0 and 10).
• A withResource method for any resource, which calls a closure with the resource passed as
a parameter, and ensures that the resource is properly deleted when the closure completes
(analagous to the default Groovy method InputStream.withStream).
For full details, see the source code or javadoc documentation for these two classes.
GATE Embedded
7.17
179
Saving Config Data to gate.xml
Arbitrary feature/value data items can be saved to the user’s gate.xml file via the following
API calls:
To get the config data: Map configData = Gate.getUserConfig().
To add config data simply put pairs into the map: configData.put("my new config key",
"value");.
To write the config data back to the XML file: Gate.writeUserConfig();.
Note that new config data will simply override old values, where the keys are the same. In
this way defaults can be set up by putting their values in the main gate.xml file, or the site
gate.xml file; they can then be overridden by the user’s gate.xml file.
7.18
Annotation merging through the API
If we have annotations about the same subject on the same document from different annotators, we may need to merge those annotations to form a unified annotation. Two
approaches for merging annotations are implemented in the API, via static methods in the
class gate.util.AnnotationMerging.
The two methods have very similar input and output parameters. Each of the methods
takes an array of annotation sets, which should be the same annotation type on the same
document from different annotators, as input. A single feature can also be specified as a
parameter (or given asnull if no feature is to be specified).
The output is a map, the key of which is one merged annotation and the value of which
represents the annotators (in terms of the indices of the array of annotation sets) who support the annotation. The methods also have a boolean input parameter to indicate whether
or not the annotations from different annotators are based on the same set of instances,
which can be determined by the static method public boolean isSameInstancesForAnnotators(AnnotationSet[] annsA) in the class gate.util.IaaCalculation. One instance corresponds to all the annotations with the same span. If the annotation sets are based on the
same set of instances, the merging methods will ensure that the merged annotations are on
the same set of instances.
The two methods corresponding to those described for the Annotation Merging plugin described in Section 20.22. They are:
• The Method public static void mergeAnnotation(AnnotationSet[] annsArr, String
nameFeat, HashMap<Annotation,String>mergeAnns, int numMinK, boolean isTheSameInstances) merges the annotations stored in the array annsArr. The merged
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annotation is put into the map mergeAnns, with a key of the merged annotation and
value of a string containing the indices of elements in the annotation set array annsArr
which contain that annotation. NumMinK specifies the minimal number of the annotators supporting one merged annotation. The boolean parameter isTheSameInstances
indicate if or not those annotation sets for merging are based on the same instances.
• Method public static void mergeAnnotationMajority(AnnotationSet[] annsArr, String
nameFeat, HashMap<Annotation, String>mergeAnns, boolean isTheSameInstances)
selects the annotations which the majority of the annotators agree on. The meanings
of parameters are the same as those in the above method.
Chapter 8
JAPE: Regular Expressions over
Annotations
If Osama bin Laden did not exist, it would be necessary to invent him. For the
past four years, his name has been invoked whenever a US president has sought
to increase the defence budget or wriggle out of arms control treaties. He has
been used to justify even President Bush’s missile defence programme, though
neither he nor his associates are known to possess anything approaching ballistic
missile technology. Now he has become the personification of evil required to
launch a crusade for good: the face behind the faceless terror.
The closer you look, the weaker the case against Bin Laden becomes. While
the terrorists who inflicted Tuesday’s dreadful wound may have been inspired by
him, there is, as yet, no evidence that they were instructed by him. Bin Laden’s
presumed guilt appears to rest on the supposition that he is the sort of man who
would have done it. But his culpability is irrelevant: his usefulness to western
governments lies in his power to terrify. When billions of pounds of military
spending are at stake, rogue states and terrorist warlords become assets precisely
because they are liabilities.
The need for dissent, George Monbiot, The Guardian, Tuesday September 18,
2001.
JAPE is a Java Annotation Patterns Engine. JAPE provides finite state transduction over
annotations based on regular expressions. JAPE is a version of CPSL – Common Pattern
Specification Language1 . This chapter introduces JAPE, and outlines the functionality available. (You can find an excellent tutorial here; thanks to Dhaval Thakker, Taha Osmin and
Phil Lakin).
1
A good description of the original version of this language is in Doug Appelt’s TextPro manual. Doug
was a great help to us in implementing JAPE. Thanks Doug!
181
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JAPE: Regular Expressions over Annotations
JAPE allows you to recognise regular expressions in annotations on documents. Hang on,
there’s something wrong here: a regular language can only describe sets of strings, not graphs,
and GATE’s model of annotations is based on graphs. Hmmm. Another way of saying this:
typically, regular expressions are applied to character strings, a simple linear sequence of
items, but here we are applying them to a much more complex data structure. The result is
that in certain cases the matching process is non-deterministic (i.e. the results are dependent
on random factors like the addresses at which data is stored in the virtual machine): when
there is structure in the graph being matched that requires more than the power of a regular
automaton to recognise, JAPE chooses an alternative arbitrarily. However, this is not the
bad news that it seems to be, as it turns out that in many useful cases the data stored in
annotation graphs in GATE (and other language processing systems) can be regarded as
simple sequences, and matched deterministically with regular expressions.
A JAPE grammar consists of a set of phases, each of which consists of a set of pattern/action rules. The phases run sequentially and constitute a cascade of finite state transducers
over annotations. The left-hand-side (LHS) of the rules consist of an annotation pattern
description. The right-hand-side (RHS) consists of annotation manipulation statements.
Annotations matched on the LHS of a rule may be referred to on the RHS by means of
labels that are attached to pattern elements. Consider the following example:
Phase: Jobtitle
Input: Lookup
Options: control = appelt debug = true
Rule: Jobtitle1
(
{Lookup.majorType == jobtitle}
(
{Lookup.majorType == jobtitle}
)?
)
:jobtitle
-->
:jobtitle.JobTitle = {rule = "JobTitle1"}
The LHS is the part preceding the ‘-->’ and the RHS is the part following it. The LHS specifies a pattern to be matched to the annotated GATE document, whereas the RHS specifies
what is to be done to the matched text. In this example, we have a rule entitled ‘Jobtitle1’,
which will match text annotated with a ‘Lookup’ annotation with a ‘majorType’ feature of
‘jobtitle’, followed optionally by further text annotated as a ‘Lookup’ with ‘majorType’ of
‘jobtitle’. Once this rule has matched a sequence of text, the entire sequence is allocated a
label by the rule, and in this case, the label is ‘jobtitle’. On the RHS, we refer to this span
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of text using the label given in the LHS; ‘jobtitle’. We say that this text is to be given an
annotation of type ‘JobTitle’ and a ‘rule’ feature set to ‘JobTitle1’.
We began the JAPE grammar by giving it a phase name, e.g. ‘Phase: Jobtitle’. JAPE
grammars can be cascaded, and so each grammar is considered to be a ‘phase’ (see Section 8.5). The phase name makes up part of the Java class name for the compiled RHS
actions. Because of this, it must contain alphanumeric characters and underscores only, and
cannot start with a number.
We also provide a list of the annotation types we will use in the grammar. In this case,
we say ‘Input: Lookup’ because the only annotation type we use on the LHS are Lookup
annotations. If no annotations are defined, all annotations will be matched.
Then, several options are set:
• Control; in this case, ‘appelt’. This defines the method of rule matching (see Section
8.4)
• Debug. When set to true, if the grammar is running in Appelt mode and there is more
than one possible match, the conflicts will be displayed on the standard output.
A wide range of functionality can be used with JAPE, making it a very powerful system.
Section 8.1 gives an overview of some common LHS tasks. Section 8.2 talks about the various
operators available for use on the LHS. After that, Section 8.3 outlines RHS functionality.
Section 8.4 talks about priority and Section 8.5 talks about phases. Section 8.6 talks about
using Java code on the RHS, which is the main way of increasing the power of the RHS. We
conclude the chapter with some miscellaneous JAPE-related topics of interest.
8.1
The Left-Hand Side
The LHS of a JAPE grammar aims to match the text span to be annotated, whilst avoiding
undesirable matches. There are various tools available to enable you to do this. This section
outlines how you would approach various common tasks on the LHS of your JAPE grammar.
8.1.1
Matching a Simple Text String
To match a simple text string, you need to refer to a feature on an annotation that contains
the string; for example,
{Token.string == "of"}
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The following grammar shows a sequence of strings being matched. Bracketing, along with
the ‘or’ operator, is used to define how the strings should come together:
Phase: UrlPre
Input: Token SpaceToken
Options: control = appelt
Rule: Urlpre
( (({Token.string == "http"} |
{Token.string == "ftp"})
{Token.string == ":"}
{Token.string == "/"}
{Token.string == "/"}
) |
({Token.string == "www"}
{Token.string == "."}
)
):urlpre
-->
:urlpre.UrlPre = {rule = "UrlPre"}
Alternatively you could use the ‘string’ metaproperty. See Section 8.1.4 for an example of
using metaproperties.
8.1.2
Matching Entire Annotation Types
You can specify the presence of an annotation previously assigned from a gazetteer, tokeniser,
or other module. For example, the following will match a Lookup annotation:
{Lookup}
The following will match if there is not a Lookup annotation at this location:
{!Lookup}
The following rule shows several different annotation types being matched. We also see a
string being matched, and again, the use of the ‘or’ operator:
Rule: Known
Priority: 100
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185
(
{Location}|
{Person}|
{Date}|
{Organization}|
{Address}|
{Money} |
{Percent}|
{Token.string == "Dear"}|
{JobTitle}|
{Lookup}
):known
-->
{}
8.1.3
Using Attributes and Values
You can specify the attributes (and values) of an annotation to be matched. Several operators
are supported; see Section 8.2 for full details:
• {Token.kind == "number"}, {Token.length != 4} - equality and inequality.
• {Token.string > "aardvark"}, {Token.length < 10} - comparison operators. >=
and <= are also supported.
• {Token.string =~ "[Dd]ogs"}, {Token.string !~ "(?i)hello"} - regular expression. ==~ and !=~ are also provided, for whole-string matching.
• {X contains Y} and {X within Y} for checking annotations within the context of
other annotations.
In the following rule, the ‘category’ feature of the ‘Token’ annotation is used, along with the
‘equals’ operator:
Rule: Unknown
Priority: 50
(
{Token.category == NNP}
)
:unknown
-->
:unknown.Unknown = {kind = "PN", rule = Unknown}
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8.1.4
Using Meta-Properties
In addition to referencing annotation features, JAPE allows access to other ‘meta-properties’
of an annotation. This is done by using an ‘@’ symbol rather than a ‘.’ symbol after the
annotation type name. The three meta-properties that are built in are:
• length - returns the spanning length of the annotation.
• string - returns the string spanned by the annotation in the document.
• cleanString - Like string, but with extra white space stripped out. (i.e. ‘\s+’ goes to
a single space and leading or trailing white space is removed).
{X@length > 5}:label-->:label.New = {}
@string is also available in assignments on the right-hand side:
{X@length > 5}:label-->:label.New = {somefeat = :label.X@string }
As well as getting the string covered by a single annotation it is also possible to omit the
annotation type and get the string spanned by all the annotations bound to a label, for
example:
(
{X@length > 5}
({Y}+):ys
):label
-->
:label.New = { somefeat = :ys@string }
If several Y annotations were included in the match, the New annotation’s feature would be
set to the string starting at the beginning of the leftmost Y that was matched and ending
at the end of the rightmost one.
The ‘meta-properties’ @length and @cleanString are not available in the RHS but may be
added in the future.
8.1.5
Using Templates
In cases where a grammar contains many similar or identical strings or other literal values,
JAPE supports the concept of templates. A template is a named value declared in the
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187
grammar file, similar to a variable in Java or other programming languages, which can be
referenced anywhere where a normal string literal, boolean or numeric value could be used,
on the left- or right-hand side of a rule. In the simplest case templates can be constants:
Template: source = "Interesting entity finder"
Template: threshold = 0.6
The templates can be used in rules by providing their names in square brackets:
Rule: InterestingLocation
(
{Location.score >= [threshold]}
):loc
-->
:loc.Entity = { type = Location, source = [source] }
The JAPE grammar parser substitutes the template values for their references when the
grammar is parsed. Thus the example rule is equivalent to
Rule: InterestingLocation
(
{Location.score >= 0.6}
):loc
-->
:loc.Entity = { type = Location,
source = "Interesting entity finder" }
The advantage of using templates is that if there are many rules in the grammar that all
reference the threshold template then it is possible to change the threshold for all rules by
simply changing the template definition.
The name “template” stems from the fact that templates whose value is a string can contain
parameters, specified using ${name} notation:
Template: url = "http://gate.ac.uk/${path}"
When a template containing parameters is referenced, values for the parameters may be
specified:
...
-->
:anchor.Reference = {
page = [url path = "userguide"] }
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This is equivalent to page = "http://gate.ac.uk/userguide". Multiple parameter value
assignments are separated by commas, for example:
Template: proton =
"http://proton.semanticweb.org/2005/04/proton${mod}#${n}"
...
{Lookup.class == [proton mod="km", n="Mention"]}
// equivalent to
// {Lookup.class ==
//
"http://proton.semanticweb.org/2005/04/protonkm#Mention"}
The parser will report an error if a value is specified for a parameter that is not declared by
the referenced template, for example [proton module="km"] would not be permitted in the
above example.
Advanced template usage
If a template contains parameters for which values are not provided when the template is
referenced, the parameter placeholders are passed through unchanged. Combined with the
fact that the value for a template definition can itself be a reference to a previously-defined
template, this allows for idioms like the following:
Template: proton =
"http://proton.semanticweb.org/2005/04/proton${mod}#${n}"
Template: pkm = [proton mod="km"]
Template: ptop = [proton mod="t"]
...
({Lookup.class == [ptop n="Person"]}):look
-->
:look.Mention = { class = [pkm n="Mention"], of = "Person"}
(This example is inspired by the ontology-aware JAPE matching mode described in section 14.10.)
In a multi-phase JAPE grammar, templates defined in earlier phases may be referenced in
later phases. This makes it possible to declare constants (such as the PROTON URIs above)
in one place and reference them throughout a complex grammar.
JAPE: Regular Expressions over Annotations
8.1.6
189
Multiple Pattern/Action Pairs
It is also possible to have more than one pattern and corresponding action, as shown in the
rule below. On the LHS, each pattern is enclosed in a set of round brackets and has a unique
label; on the RHS, each label is associated with an action. In this example, the Lookup
annotation is labelled ‘jobtitle’ and is given the new annotation JobTitle; the TempPerson
annotation is labelled ‘person’ and is given the new annotation ‘Person’.
Rule: PersonJobTitle
Priority: 20
(
{Lookup.majorType == jobtitle}
):jobtitle
(
{TempPerson}
):person
-->
:jobtitle.JobTitle = {rule = "PersonJobTitle"},
:person.Person = {kind = "personName", rule = "PersonJobTitle"}
Similarly, labelled patterns can be nested, as in the example below, where the whole pattern
is annotated as Person, but within the pattern, the jobtitle is annotated as JobTitle.
Rule: PersonJobTitle2
Priority: 20
(
(
{Lookup.majorType == jobtitle}
):jobtitle
{TempPerson}
):person
-->
:jobtitle.JobTitle = {rule = "PersonJobTitle"},
:person.Person = {kind = "personName", rule = "PersonJobTitle"}
8.1.7
LHS Macros
Macros allow you to create a definition that can then be used multiple times in
your JAPE rules. In the following JAPE grammar, we have a cascade of macros
used. The macro ‘AMOUNT NUMBER’ makes use of the macros ‘MILLION BILLION’
and ‘NUMBER WORDS’, and the rule ‘MoneyCurrencyUnit’ then makes use of
‘AMOUNT NUMBER’:
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JAPE: Regular Expressions over Annotations
Phase: Number
Input: Token Lookup
Options: control = appelt
Macro: MILLION_BILLION
({Token.string == "m"}|
{Token.string == "million"}|
{Token.string == "b"}|
{Token.string == "billion"}|
{Token.string == "bn"}|
{Token.string == "k"}|
{Token.string == "K"}
)
Macro: NUMBER_WORDS
(
(({Lookup.majorType == number}
({Token.string == "-"})?
)*
{Lookup.majorType == number}
{Token.string == "and"}
)*
({Lookup.majorType == number}
({Token.string == "-"})?
)*
{Lookup.majorType == number}
)
Macro: AMOUNT_NUMBER
(({Token.kind == number}
(({Token.string == ","}|
{Token.string == "."}
)
{Token.kind == number}
)*
|
(NUMBER_WORDS)
)
(MILLION_BILLION)?
)
Rule: MoneyCurrencyUnit
(
(AMOUNT_NUMBER)
({Lookup.majorType == currency_unit})
)
:number -->
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191
:number.Money = {kind = "number", rule = "MoneyCurrencyUnit"}
8.1.8
Using Context
Context can be dealt with in the grammar rules in the following way. The pattern to be
annotated is always enclosed by a set of round brackets. If preceding context is to be included
in the rule, this is placed before this set of brackets. This context is described in exactly
the same way as the pattern to be matched. If context following the pattern needs to be
included, it is placed after the label given to the annotation. Context is used where a pattern
should only be recognised if it occurs in a certain situation, but the context itself does not
form part of the pattern to be annotated.
For example, the following rule for Time (assuming an appropriate macro for ‘year’) would
mean that a year would only be recognised if it occurs preceded by the words ‘in’ or ‘by’:
Rule: YearContext1
({Token.string == "in"}|
{Token.string == "by"}
)
(YEAR)
:date -->
:date.Timex = {kind = "date", rule = "YearContext1"}
Similarly, the following rule (assuming an appropriate macro for ‘email’) would mean that
an email address would only be recognised if it occurred inside angled brackets (which would
not themselves form part of the entity):
Rule: Emailaddress1
({Token.string == ‘<’})
(
(EMAIL)
)
:email
({Token.string == ‘>’})
-->
:email.Address= {kind = "email", rule = "Emailaddress1"}
Also, it is possible to specify the constraint that one annotation must start at the same place
as another. For example:
Rule: SurnameStartingWithDe
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JAPE: Regular Expressions over Annotations
(
{Token.string == "de",
Lookup.majorType == "name",
Lookup.minorType == "surname"}
):de
-->
:de.Surname = {prefix = "de"}
This rule would match anywhere where a Token with string ‘de’ and a Lookup with majorType ‘name’ and minorType ‘surname’ start at the same offset in the text. Both the
Lookup and Token annotations would be included in the :de binding, so the Surname annotation generated would span the longer of the two. Constraints on the same annotation type
must be satisfied by a single annotation, so in this example there must be a single Lookup
matching both the major and minor types – the rule would not match if there were two
different lookups at the same location, one of them satisfying each constraint.
It is important to remember that context is consumed by the rule, so it cannot be reused in
another rule within the same phase. So, for example, right context cannot be used as left
context for another rule.
8.1.9
Multi-Constraint Statements
In the examples we have seen so far, most statements have contained only one constraint.
For example, in this statement, the ‘category’ of ‘Token’ must equal ‘NNP’:
Rule: Unknown
Priority: 50
(
{Token.category == NNP}
)
:unknown
-->
:unknown.Unknown = {kind = "PN", rule = Unknown}
However, it is equally acceptable to have multiple constraints in a statement. In this example,
the ‘majorType’ of ‘Lookup’ must be ‘name’ and the ‘minorType’ must be ‘surname’:
Rule: Surname
(
{Lookup.majorType == "name",
Lookup.minorType == "surname"}
):surname
-->
:surname.Surname = {}
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193
As we saw in Section 8.1.8, the constraints may refer to different annotations. In this
example, in addition to the constraints on the ‘majorType’ and ‘minorType’ of ‘Lookup’, we
also have a constraint on the ‘string’ of ‘Token’:
Rule: SurnameStartingWithDe
(
{Token.string == "de",
Lookup.majorType == "name",
Lookup.minorType == "surname"}
):de
-->
:de.Surname = {prefix = "de"}
8.1.10
Negation
All the examples in the preceding sections involve constraints that require the presence of
certain annotations to match. JAPE also supports ‘negative’ constraints which specify the
absence of annotations. A negative constraint is signalled in the grammar by a ‘!’ character.
Negative constraints are generally used in combination with positive ones to constrain the
locations at which the positive constraint can match. For example:
Rule: PossibleName
(
{Token.orth == "upperInitial", !Lookup}
):name
-->
:name.PossibleName = {}
This rule would match any uppercase-initial Token, but only where there is no Lookup annotation starting at the same location. The general rule is that a negative constraint matches
at any location where the corresponding positive constraint would not match. Negative constraints do not contribute any annotations to the bindings - in the example above, the :name
binding would contain only the Token annotation. The exception to this is when a negative
constraint is used alone, without any positive constraints in the combination. In this case
it binds all the annotations at the match position that do not match the constraint. Thus,
{!Lookup} would bind all the annotations starting at this location except Lookups. In most
cases, negative constraints should only be used in combination with positive ones.
Any constraint can be negated, for example:
Rule: SurnameNotStartingWithDe
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JAPE: Regular Expressions over Annotations
(
{Surname, !Token.string ==~ "[Dd]e"}
):name
-->
:name.NotDe = {}
This would match any Surname annotation that does not start at the same place
as a Token with the string ‘de’ or ‘De’. Note that this is subtly different from
{Surname, Token.string !=~ "[Dd]e"}, as the second form requires a Token annotation
to be present, whereas the first form (!Token...) will match if there is no Token annotation
at all at this location.2
Although JAPE provides an operator to look for the absence of a single annotation type,
there is no support for a general negative operator to prevent a rule from firing if a particular
sequence of annotations is found. One solution to this is to create a ‘negative rule’ which
has higher priority than the matching ‘positive rule’. The style of matching must be Appelt
for this to work. To create a negative rule, simply state on the LHS of the rule the pattern
that should NOT be matched, and on the RHS do nothing. In this way, the positive rule
cannot be fired if the negative pattern matches, and vice versa, which has the same end
result as using a negative operator. A useful variation for developers is to create a dummy
annotation on the RHS of the negative rule, rather than to do nothing, and to give the
dummy annotation a rule feature. In this way, it is obvious that the negative rule has fired.
Alternatively, use Java code on the RHS to print a message when the rule fires. An example
of a matching negative and positive rule follows. Here, we want a rule which matches a
surname followed by a comma and a set of initials. But we want to specify that the initials
shouldn’t have the POS category PRP (personal pronoun). So we specify a negative rule
that will fire if the PRP category exists, thereby preventing the positive rule from firing.
Rule: NotPersonReverse
Priority: 20
// we don’t want to match ’Jones, I’
(
{Token.category == NNP}
{Token.string == ","}
{Token.category == PRP}
)
:foo
-->
{}
Rule:
PersonReverse
Priority: 5
// we want to match ‘Jones, F.W.’
(
2
In the Montreal transducer, the two forms were equivalent
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195
{Token.category == NNP}
{Token.string == ","}
(INITIALS)?
)
:person -->
8.1.11
Escaping Special Characters
To specify a single or double quote as a string, precede it with a backslash, e.g.
{Token.string=="\""}
will match a double quote. For other special characters, such as ‘$’, enclose it in double
quotes, e.g.
{Token.category == "PRP$"}
8.2
LHS Operators in Detail
This section gives more detail on the behaviour of the matching operators used on the lefthand side of JAPE rules.
8.2.1
Compositional Operators
Compositional operators are used to combine matching constructions in the manner intended.
Union and Kleene operators are available, as is range notation.
Union and Kleene Operators
The following union and Kleene operators are available:
• | - or
• * - zero or more occurrences
• ? - zero or one occurrences
• + - one or more occurrences
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In the following example, you can see the ‘|’ and ‘?’ operators being used:
Rule: LocOrganization
Priority: 50
(
({Lookup.majorType == location} |
{Lookup.majorType == country_adj})
{Lookup.majorType == organization}
({Lookup.majorType == organization})?
)
:orgName -->
:orgName.TempOrganization = {kind = "orgName", rule=LocOrganization}
Range Notation
A range notation can also be added. e.g.
({Token})[1,3]
matches one to three Tokens in a row.
({Token.kind == number})[3]
matches exactly 3 number Tokens in a row.
8.2.2
Matching Operators
Matching operators are used to specify how matching must take place between a specification
and an annotation in the document. Equality (‘==’ and ‘!=’) and comparison (‘<’, ‘<=’,
‘>=’ and ‘>’) operators can be used, as can regular expression matching and contextual
operators (‘contains’ and ‘within’).
Equality Operators
The equality operators are ‘==’ and ‘!=’. The basic operator in JAPE is equality.
{Lookup.majorType == "person"} matches a Lookup annotation whose majorType feature has the value ‘person’. Similarly {Lookup.majorType != "person"} would match any
Lookup whose majorType feature does not have the value ‘person’. If a feature is missing
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197
it is treated as if it had an empty string as its value, so this would also match a Lookup
annotation that did not have a majorType feature at all.
Certain type coercions are performed:
• If the constraint’s attribute is a string, it is compared with the annotation feature value
using string equality (String.equals()).
• If the constraint’s attribute is an integer it is treated as a java.lang.Long. If the
annotation feature value is also a Long, or is a string that can be parsed as a Long,
then it is compared using Long.equals().
• If the constraint’s attribute is a floating-point number it is treated as a
java.lang.Double. If the annotation feature value is also a Double, or is a string that
can be parsed as a Double, then it is compared using Double.equals().
• If the constraint’s attribute is true or false (without quotes) it is treated as a
java.lang.Boolean. If the annotation feature value is also a Boolean, or is a string
that can be parsed as a Boolean, then it is compared using Boolean.equals().
The != operator matches exactly when == doesn’t.
Comparison Operators
The comparison operators are ‘<’, ‘<=’, ‘>=’ and ‘>’. Comparison operators have their
expected meanings, for example {Token.length > 3} matches a Token annotation whose
length attribute is an integer greater than 3. The behaviour of the operators depends on the
type of the constraint’s attribute:
• If the constraint’s attribute is a string it is compared with the annotation feature value
using Unicode-lexicographic order (see String.compareTo()).
• If the constraint’s attribute is an integer it is treated as a java.lang.Long. If the
annotation feature value is also a Long, or is a string that can be parsed as a Long,
then it is compared using Long.compareTo().
• If the constraint’s attribute is a floating-point number it is treated as a
java.lang.Double. If the annotation feature value is also a Double, or is a string that
can be parsed as a Double, then it is compared using Double.compareTo().
Regular Expression Operators
The regular expression operators are ‘=∼’, ‘==∼’, ‘!∼’ and ‘!=∼’. These operators match
regular expressions. {Token.string =~ "[Dd]ogs"} matches a Token annotation whose
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JAPE: Regular Expressions over Annotations
string feature contains a substring that matches the regular expression [Dd]ogs, using
!~ would match if the feature value does not contain a substring that matches the regular expression. The ==~ and !=~ operators are like =~ and !~ respectively, but require
that the whole value match (or not match) the regular expression3 . As with ==, missing features are treated as if they had the empty string as their value, so the constraint
{Identifier.name ==~ "(?i)[aeiou]*"} would match an Identifier annotation which does
not have a name feature, as well as any whose name contains only vowels.
The matching uses the standard Java regular expression library, so full details of the pattern
syntax can be found in the JavaDoc documentation for java.util.regex.Pattern. There are a
few specific points to note:
• To enable flags such as case-insensitive matching you can use the (?flags) notation.
See the Pattern JavaDocs for details.
• If you need to include a double quote character in a regular expression you must precede it with a backslash, otherwise JAPE will give a syntax error. Quoted strings in
JAPE grammars also convert the sequences \n, \r and \t to the characters newline
(U+000A), carriage return (U+000D) and tab (U+0009) respectively, but these characters can match literally in regular expressions so it does not make any difference to
the result in most cases.4
Contextual Operators
The contextual Operators are ‘contains’ and ‘within’. These operators match annotations
within the context of other annotations.
• contains - Written as {X contains Y}, returns true if an annotation of type X completely contains an annotation of type Y.
• within - Written as {X within Y}, returns true if an annotation of type X is completely
covered by an annotation of type Y.
For either operator, the right-hand value (Y in the above examples) can be a full constraint
itself. For example {X contains {Y.foo==bar}} is also accepted. The operators can be
used in a multi-constraint statement (see Section 8.1.9) just like any of the traditional ones,
so {X.f1 != "something", X contains {Y.foo==bar}} is valid.
3
This syntax will be familiar to Groovy users.
However this does mean that it is not possible to include an n, r or t character after a backslash in a
JAPE quoted string, or to have a backslash as the last character of your regular expression. Workarounds
include placing the backslash in a character class ([\\]—) or enabling the (?x) flag, which allows you to put
whitespace between the backslash and the offending character without changing the meaning of the pattern.
4
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199
Custom Operators
It is possible to add additional custom operators without modifying the JAPE language.
There are new init-time parameters to Transducer so that additional annotation ‘metaproperty’ accessors and custom operators can be referenced at runtime. To add a custom
operator, write a class that implements gate.jape.constraint.ConstraintPredicate, make the
class available to GATE (either by putting the class in a JAR file in the lib directory or
by putting the class in a plugin and loading the plugin), and then list that class name for
the Transducer’s ‘operators’ property. Similarly, to add a custom ‘meta-property’ accessor,
write a class that implements gate.jape.constraint.AnnotationAccessor, and then list that
class name in the Transducer’s ‘annotationAccessors’ property.
8.3
The Right-Hand Side
The RHS of the rule contains information about the annotation to be created/manipulated.
Information about the text span to be annotated is transferred from the LHS of the rule
using the label just described, and annotated with the entity type (which follows it). Finally,
attributes and their corresponding values are added to the annotation. Alternatively, the
RHS of the rule can contain Java code to create or manipulate annotations, see Section 8.6.
8.3.1
A Simple Example
In the simple example below, the pattern described will be awarded an annotation of type
‘Enamex’ (because it is an entity name). This annotation will have the attribute ‘kind’,
with value ‘location’, and the attribute ‘rule’, with value ‘GazLocation’. (The purpose of
the ‘rule’ attribute is simply to ease the process of manual rule validation).
Rule: GazLocation
(
{Lookup.majorType == location}
)
:location -->
:location.Enamex = {kind="location", rule=GazLocation}
8.3.2
Copying Feature Values from the LHS to the RHS
JAPE provides limited support for copying annotation feature values from the left to the
right hand side of a rule, for example:
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Rule: LocationType
(
{Lookup.majorType == location}
):loc
-->
:loc.Location = {rule = "LocationType", type = :loc.Lookup.minorType}
This will set the ‘type’ feature of the generated location to the value of the ‘minorType’ feature from the ‘Lookup’ annotation bound to the loc label. If the Lookup has no minorType,
the Location will have no ‘type’ feature. The behaviour of newFeat = :bind.Type.oldFeat
is:
• Find all the annotations of type Type from the left hand side binding bind.
• Find one of them that has a non-null value for its oldFeat feature (if there is more
than one, which one is chosen is up to the JAPE implementation).
• If such a value exists, set the newFeat feature of our newly created annotation to this
value.
• If no such non-null value exists, do not set the newFeat feature at all.
Notice that the behaviour is deliberately underspecified if there is more than one Type annotation in bind. If you need more control, or if you want to copy several feature values from
the same left hand side annotation, you should consider using Java code on the right hand
side of your rule (see Section 8.6).
8.3.3
RHS Macros
Macros, first introduced in the context of the left-hand side (Section 8.1.7) can also be used
on the RHS of rules. In this case, the label (which matches the label on the LHS of the rule)
should be included in the macro. Below we give an example of using a macro on the RHS:
Macro: UNDERSCORES_OKAY
// separate
:match
// lines
{
AnnotationSet matchedAnns = bindings.get("match");
int begOffset = matchedAnns.firstNode().getOffset().intValue();
int endOffset = matchedAnns.lastNode().getOffset().intValue();
String mydocContent = doc.getContent().toString();
String matchedString = mydocContent.substring(begOffset, endOffset);
JAPE: Regular Expressions over Annotations
201
FeatureMap newFeatures = Factory.newFeatureMap();
if(matchedString.equals("Spanish"))
newFeatures.put("myrule", "Lower");
}
else
{
newFeatures.put("myrule", "Upper");
}
{
newFeatures.put("quality", "1");
annotations.add(matchedAnns.firstNode(), matchedAnns.lastNode(),
"Spanish_mark", newFeatures);
}
Rule: Lower
(
({Token.string == "Spanish"})
:match)-->UNDERSCORES_OKAY
// no label here, only macro name
Rule: Upper
(
({Token.string == "SPANISH"})
:match)-->UNDERSCORES_OKAY
// no label here, only macro name
8.4
Use of Priority
Each grammar has one of 5 possible control styles: ‘brill’, ‘all’, ‘first’, ‘once’ and ‘appelt’.
This is specified at the beginning of the grammar.
The Brill style means that when more than one rule matches the same region of the document,
they are all fired. The result of this is that a segment of text could be allocated more than one
entity type, and that no priority ordering is necessary. Brill will execute all matching rules
starting from a given position and will advance and continue matching from the position in
the document where the longest match finishes.
The ‘all’ style is similar to Brill, in that it will also execute all matching rules, but the
matching will continue from the next offset to the current one.
For example, where [] are annotations of type Ann
[aaa[bbb]] [ccc[ddd]]
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then a rule matching {Ann} and creating {Ann-2} for the same spans will generate:
BRILL: [aaabbb] [cccddd]
ALL: [aaa[bbb]] [ccc[ddd]]
With the ‘first’ style, a rule fires for the first match that’s found. This makes it inappropriate
for rules that end in ‘+’ or ‘?’ or ‘*’. Once a match is found the rule is fired; it does not
attempt to get a longer match (as the other two styles do).
With the ‘once’ style, once a rule has fired, the whole JAPE phase exits after the first match.
With the appelt style, only one rule can be fired for the same region of text, according to a
set of priority rules. Priority operates in the following way.
1. From all the rules that match a region of the document starting at some point X, the
one which matches the longest region is fired.
2. If more than one rule matches the same region, the one with the highest priority is
fired
3. If there is more than one rule with the same priority, the one defined earlier in the
grammar is fired.
An optional priority declaration is associated with each rule, which should be a positive integer. The higher the number, the greater the priority. By default (if the priority declaration
is missing) all rules have the priority -1 (i.e. the lowest priority).
For example, the following two rules for location could potentially match the same text.
Rule:
Location1
Priority: 25
(
({Lookup.majorType == loc_key, Lookup.minorType == pre}
{SpaceToken})?
{Lookup.majorType == location}
({SpaceToken}
{Lookup.majorType == loc_key, Lookup.minorType == post})?
)
:locName -->
:locName.Location = {kind = "location", rule = "Location1"}
Rule: GazLocation
Priority: 20
JAPE: Regular Expressions over Annotations
203
(
({Lookup.majorType == location}):location
)
-->
:location.Name = {kind = "location", rule=GazLocation}
Assume we have the text ‘China sea’, that ‘China’ is defined in the gazetteer as ‘location’,
and that sea is defined as a ‘loc key’ of type ‘post’. In this case, rule Location1 would apply,
because it matches a longer region of text starting at the same point (‘China sea’, as opposed
to just ‘China’). Now assume we just have the text ‘China’. In this case, both rules could
be fired, but the priority for Location1 is highest, so it will take precedence. In this case,
since both rules produce the same annotation, so it is not so important which rule is fired,
but this is not always the case.
One important point of which to be aware is that prioritisation only operates within a
single grammar. Although we could make priority global by having all the rules in a single
grammar, this is not ideal due to other considerations. Instead, we currently combine all the
rules for each entity type in a single grammar. An index file (main.jape) is used to define
which grammars should be used, and in which order they should be fired.
Note also that depending on the control style, firing a rule may ‘consume’ that part of the
text, making it unavailable to be matched by other rules. This can be a problem for example
if one rule uses context to make it more specific, and that context is then missed by later
rules, having been consumed due to use of for example the ‘Brill’ control style. ‘All’, on the
other hand, would allow it to be matched.
Using priority to resolve ambiguity
If the Appelt style of matching is selected, rule priority operates in the following way.
1. Length of rule – a rule matching a longer pattern will fire first.
2. Explicit priority declaration. Use the optional Priority function to assign a ranking.
The higher the number, the higher the priority. If no priority is stated, the default is
-1.
3. Order of rules. In the case where the above two factors do not distinguish between two
rules, the order in which the rules are stated applies. Rules stated first have higher
priority.
Because priority can only operate within a single grammar, this can be a problem for dealing
with ambiguity issues. One solution to this is to create a temporary set of annotations
in initial grammars, and then manipulate this temporary set in one or more later phases
(for example, by converting temporary annotations from different phases into permanent
annotations in a single final phase). See the default set of grammars for an example of this.
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JAPE: Regular Expressions over Annotations
If two possible ways of matching are found for the same text string, a conflict can arise.
Normally this is handled by the priority mechanism (test length, rule priority and finally
rule precedence). If all these are equal, Jape will simply choose a match at random and fire
it. This leads ot non-deterministic behaviour, which should be avoided.
8.5
Using Phases Sequentially
A JAPE grammar consists of a set of sequential phases. The list of phases is specified (in the
order in which they are to be run) in a file, conventionally named main.jape. When loading
the grammar into GATE, it is only necessary to load this main file – the phases will then be
loaded automatically. It is, however, possible to omit this main file, and just load the phases
individually, but this is much more time-consuming. The grammar phases do not need to be
located in the same directory as the main file, but if they are not, the relative path should
be specified for each phase.
One of the main reasons for using a sequence of phases is that a pattern can only be used
once in each phase, but it can be reused in a later phase. Combined with the fact that
priority can only operate within a single grammar, this can be exploited to help deal with
ambiguity issues.
The solution currently adopted is to write a grammar phase for each annotation type, or
for each combination of similar annotation types, and to create temporary annotations.
These temporary annotations are accessed by later grammar phases, and can be manipulated
as necessary to resolve ambiguity or to merge consecutive annotations. The temporary
annotations can either be removed later, or left and simply ignored.
Generally, annotations about which we are more certain are created earlier on. Annotations
which are more dubious may be created temporarily, and then manipulated by later phases
as more information becomes available.
An annotation generated in one phase can be referred to in a later phase, in exactly the same
way as any other kind of annotation (by specifying the name of the annotation within curly
braces). The features and values can be referred to or omitted, as with all other annotations.
Make sure that if the Input specification is used in the grammar, that the annotation to be
referred to is included in the list.
8.6
Using Java Code on the RHS
The RHS of a JAPE rule can consist of any Java code. This is useful for removing temporary
annotations and for percolating and manipulating features from previous annotations. In
the example below
JAPE: Regular Expressions over Annotations
205
The first rule below shows a rule which matches a first person name, e.g. ‘Fred’, and adds
a gender feature depending on the value of the minorType from the gazetteer list in which
the name was found. We first get the bindings associated with the person label (i.e. the
Lookup annotation). We then create a new annotation called ‘personAnn’ which contains
this annotation, and create a new FeatureMap to enable us to add features. Then we get the
minorType features (and its value) from the personAnn annotation (in this case, the feature
will be ‘gender’ and the value will be ‘male’), and add this value to a new feature called
‘gender’. We create another feature ‘rule’ with value ‘FirstName’. Finally, we add all the
features to a new annotation ‘FirstPerson’ which attaches to the same nodes as the original
‘person’ binding.
Note that inputAS and outputAS represent the input and output annotation set. Normally,
these would be the same (by default when using ANNIE, these will be the ‘Default’ annotation set). Since the user is at liberty to change the input and output annotation sets in the
parameters of the JAPE transducer at runtime, it cannot be guaranteed that the input and
output annotation sets will be the same, and therefore we must specify the annotation set
we are referring to.
Rule: FirstName
(
{Lookup.majorType == person_first}
):person
-->
{
AnnotationSet person = bindings.get("person");
Annotation personAnn = person.iterator().next();
FeatureMap features = Factory.newFeatureMap();
features.put("gender", personAnn.getFeatures().get("minorType"));
features.put("rule", "FirstName");
outputAS.add(person.firstNode(), person.lastNode(), "FirstPerson",
features);
}
The second rule (contained in a subsequent grammar phase) makes use of annotations produced by the first rule described above. Instead of percolating the minorType from the
annotation produced by the gazetteer lookup, this time it percolates the feature from the
annotation produced by the previous grammar rule. So here it gets the ‘gender’ feature value
from the ‘FirstPerson’ annotation, and adds it to a new feature (again called ‘gender’ for
convenience), which is added to the new annotation (in outputAS) ‘TempPerson’. At the
end of this rule, the existing input annotations (from inputAS) are removed because they are
no longer needed. Note that in the previous rule, the existing annotations were not removed,
because it is possible they might be needed later on in another grammar phase.
Rule: GazPersonFirst
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JAPE: Regular Expressions over Annotations
(
{FirstPerson}
)
:person
-->
{
AnnotationSet person = bindings.get("person");
Annotation personAnn = person.iterator().next();
FeatureMap features = Factory.newFeatureMap();
features.put("gender", personAnn.getFeatures().get("gender"));
features.put("rule", "GazPersonFirst");
outputAS.add(person.firstNode(), person.lastNode(), "TempPerson",
features);
inputAS.removeAll(person);
}
You can combine Java blocks and normal assignments (separating each block or assignment
from the next with a comma), so the above RHS could be more simply expressed as
-->
:person.TempPerson = { gender = :person.FirstPerson.gender,
rule = "GazPersonFirst" },
{
inputAS.removeAll(bindings.get("person"));
}
8.6.1
A More Complex Example
The example below is more complicated, because both the title and the first name (if present)
may have a gender feature. There is a possibility of conflict since some first names are
ambiguous, or women are given male names (e.g. Charlie). Some titles are also ambiguous,
such as ‘Dr’, in which case they are not marked with a gender feature. We therefore take the
gender of the title in preference to the gender of the first name, if it is present. So, on the
RHS, we first look for the gender of the title by getting all Title annotations which have a
gender feature attached. If a gender feature is present, we add the value of this feature to a
new gender feature on the Person annotation we are going to create. If no gender feature is
present, we look for the gender of the first name by getting all firstPerson annotations which
have a gender feature attached, and adding the value of this feature to a new gender feature
on the Person annotation we are going to create. If there is no firstPerson annotation and
the title has no gender information, then we simply create the Person annotation with no
gender feature.
Rule: PersonTitle
JAPE: Regular Expressions over Annotations
Priority: 35
/* allows Mr. Jones, Mr Fred Jones etc. */
(
(TITLE)
(FIRSTNAME | FIRSTNAMEAMBIG | INITIALS2)*
(PREFIX)?
{Upper}
({Upper})?
(PERSONENDING)?
)
:person -->
{
FeatureMap features = Factory.newFeatureMap();
AnnotationSet personSet = bindings.get("person");
// get all Title annotations that have a gender feature
HashSet fNames = new HashSet();
fNames.add("gender");
AnnotationSet personTitle = personSet.get("Title", fNames);
// if the gender feature exists
if (personTitle != null && personTitle.size()>0)
{
Annotation personAnn = personTitle.iterator().next();
features.put("gender", personAnn.getFeatures().get("gender"));
}
else
{
// get all firstPerson annotations that have a gender feature
AnnotationSet firstPerson = personSet.get("FirstPerson", fNames);
if (firstPerson != null && firstPerson.size()>0)
// create a new gender feature and add the value from firstPerson
{
Annotation personAnn = firstPerson.iterator().next();
features.put("gender", personAnn.getFeatures().get("gender"));
}
}
// create some other features
features.put("kind", "personName");
features.put("rule", "PersonTitle");
// create a Person annotation and add the features we’ve created
outputAS.add(personSet.firstNode(), personSet.lastNode(), "TempPerson",
features);
}
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8.6.2
JAPE: Regular Expressions over Annotations
Adding a Feature to the Document
This is useful when using conditional controllers, where we only want to fire a particular
resource under certain conditions. We first test the document to see whether it fulfils these
conditions or not, and attach a feature to the document accordingly.
In the example below, we test whether the document contains an annotation of type ‘message’. In emails, there is often an annotation of this type (produced by the document format
analysis when the document is loaded in GATE). Note that annotations produced by document format analysis are placed automatically in the ‘Original markups’ annotation set,
so we must ensure that when running the processing resource containing this grammar that
we specify the Original markups set as the input annotation set. It does not matter what
we specify as the output annotation set, because the annotation we produce is going to
be attached to the document and not to an output annotation set. In the example, if an
annotation of type ‘message’ is found, we add the feature ‘genre’ with value ‘email’ to the
document.
Rule: Email
Priority: 150
(
{message}
)
-->
{
doc.getFeatures().put("genre", "email");
}
8.6.3
Finding the Tokens of a Matched Annotation
In this section we will demonstrate how by using Java on the right-hand side one can find all
Token annotations that are covered by a matched annotation, e.g., a Person or an Organization. This is useful if one wants to transfer some information from the matched annotations
to the tokens. For example, to add to the Tokens a feature indicating whether or not they
are covered by a named entity annotation deduced by the rule-based system. This feature
can then be given as a feature to a learning PR, e.g. the HMM. Similarly, one can add a
feature to all tokens saying which rule in the rule based system did the match, the idea being
that some rules might be more reliable than others. Finally, yet another useful feature might
be the length of the coreference chain in which the matched entity is involved, if such exists.
The example below is one of the pre-processing JAPE grammars used by the HMM application. To inspect all JAPE grammars, see the muse/applications/hmm directory in the
distribution.
JAPE: Regular Expressions over Annotations
Phase:
NEInfo
Input: Token Organization Location Person
Options: control = appelt
Rule:
NEInfo
Priority:100
({Organization} | {Person} | {Location}):entity
-->
{
//get the annotation set
AnnotationSet annSet = bindings.get("entity");
//get the only annotation from the set
Annotation entityAnn = annSet.iterator().next();
AnnotationSet tokenAS = inputAS.get("Token",
entityAnn.getStartNode().getOffset(),
entityAnn.getEndNode().getOffset());
List<Annotation> tokens = new ArrayList<Annotation>(tokenAS);
//if no tokens to match, do nothing
if (tokens.isEmpty())
return;
Collections.sort(tokens, new gate.util.OffsetComparator());
Annotation curToken=null;
for (int i=0; i < tokens.size(); i++) {
curToken = tokens.get(i);
String ruleInfo = (String) entityAnn.getFeatures().get("rule1");
String NMRuleInfo = (String) entityAnn.getFeatures().get("NMRule");
if ( ruleInfo != null) {
curToken.getFeatures().put("rule_NE_kind", entityAnn.getType());
curToken.getFeatures().put("NE_rule_id", ruleInfo);
}
else if (NMRuleInfo != null) {
curToken.getFeatures().put("rule_NE_kind", entityAnn.getType());
curToken.getFeatures().put("NE_rule_id", "orthomatcher");
}
else {
curToken.getFeatures().put("rule_NE_kind", "None");
curToken.getFeatures().put("NE_rule_id", "None");
}
List matchesList = (List) entityAnn.getFeatures().get("matches");
if (matchesList != null) {
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JAPE: Regular Expressions over Annotations
if (matchesList.size() == 2)
curToken.getFeatures().put("coref_chain_length", "2");
else if (matchesList.size() > 2 && matchesList.size() < 5)
curToken.getFeatures().put("coref_chain_length", "3-4");
else
curToken.getFeatures().put("coref_chain_length", "5-more");
}
else
curToken.getFeatures().put("coref_chain_length", "0");
}//for
}
Rule:
TokenNEInfo
Priority:10
({Token}):entity
-->
{
//get the annotation set
AnnotationSet annSet = bindings.get("entity");
//get the only annotation from the set
Annotation entityAnn = annSet.iterator().next();
entityAnn.getFeatures().put("rule_NE_kind", "None");
entityAnn.getFeatures().put("NE_rule_id", "None");
entityAnn.getFeatures().put("coref_chain_length", "0");
}
8.6.4
Using Named Blocks
For the common case where a Java block refers just to the annotations from a single lefthand-side binding, JAPE provides a shorthand notation:
Rule: RemoveDoneFlag
(
{Instance.flag == "done"}
):inst
-->
:inst{
Annotation theInstance = instAnnots.iterator().next();
theInstance.getFeatures().remove("flag");
}
JAPE: Regular Expressions over Annotations
211
This rule is equivalent to the following:
Rule: RemoveDoneFlag
(
{Instance.flag == "done"}
):inst
-->
{
AnnotationSet instAnnots = bindings.get("inst");
if(instAnnots != null && instAnnots.size() != 0) {
Annotation theInstance = instAnnots.iterator().next();
theInstance.getFeatures().remove("flag");
}
}
A label :<label> on a Java block creates a local variable <label>Annots within the Java
block which is the AnnotationSet bound to the <label> label. Also, the Java code in the
block is only executed if there is at least one annotation bound to the label, so you do not
need to check this condition in your own code. Of course, if you need more flexibility, e.g.
to perform some action in the case where the label is not bound, you will need to use an
unlabelled block and perform the bindings.get() yourself.
8.6.5
Java RHS Overview
When a JAPE grammar is parsed, a Jape parser creates action classes for all Java RHSs in
the grammar. (one action class per RHS) RHS Java code will be embedded as a body of the
method doit and will work in context of this method. When a particular rule is fired, the
method doit will be executed.
Method doit is specified by the interface gate.jape.RhsAction. Each action class implements this interface and is generated with the following template:
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3
4
5
6
7
8
9
10
11
12
13
import java . io .*;
import java . util .*;
import gate .*;
import gate . jape .*;
import gate . creole . ontology .*;
import gate . annotation .*;
import gate . util .*;
// Import: block code will be embedded here
class < AutogeneratedActionClassName >
implements java . io . Serializable , gate . jape . RhsAction {
private ActionContext ctx ;
public void setActionContext () { ... }
public ActionContext getActionContext () { ... }
212
JAPE: Regular Expressions over Annotations
public void doit (
gate . Document doc ,
java . util . Map < java . lang . String , gate . AnnotationSet > bindings ,
gate . AnnotationSet annotations ,
gate . AnnotationSet inputAS ,
gate . AnnotationSet outputAS ,
gate . creole . ontology . Ontology ontology ) throws JapeException {
// your RHS Java code will be embedded here ...
}
14
15
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18
19
20
21
22
23
}
Method doit has the following parameters that can be used in RHS Java code:
• gate.Document doc - a document that is currently processed
• java.util.Map<String, AnnotationSet> bindings - a map of binding variables
where a key is a (String) name of binding variable and value is (AnnotationSet) set of
annotations corresponding to this binding variable5
• gate.AnnotationSet annotations - Do not use this (it’s a synonym for outputAS
that is still used in some grammars but is now deprecated).
• gate.AnnotationSet inputAS - input annotations
• gate.AnnotationSet outputAS - output annotations
• gate.creole.ontology.Ontology ontology - a GATE’s transducer ontology
In addition, the field ctx provides the ActionContext object to the RHS code (see the
ActionContext JavaDoc for more).
In your Java RHS you can use short names for all Java classes that are imported by the
action class (plus Java classes from the packages that are imported by default according to
JVM specification: java.lang.*, java.math.*). But you need to use fully qualified Java class
names for all other classes. For example:
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3
4
5
6
7
8
-->
{
// VALID line examples
AnnotationSet as = ...
InputStream is = ...
java . util . logging . Logger myLogger =
java . util . logging . Logger . getLogger ( " JAPELogger " );
java . sql . Statement stmt = ...
9
// INVALID line examples
Logger myLogger = Logger . getLogger ( " JapePhaseLogger " );
Statement stmt = ...
10
11
12
13
}
5
Prior to GATE 5.2 this parameter was a plain Map without type parameters, which is why you will see
a lot of now-unnecessary casts in existing JAPE grammars such as those in ANNIE.
JAPE: Regular Expressions over Annotations
213
In order to add additional Java import or import static statements to all Java RHS’ of the
rules in a JAPE grammar file, you can use the following code at the beginning of the JAPE
file:
1
2
3
4
Imports : {
import java . util . logging . Logger ;
import java . sql .*;
}
These import statements will be added to the default import statements for each action
class generated for a RHS and the corresponding classes can be used in the RHS Java
code without the need to use fully qualified names. A useful class to know about is
gate.Utils (see the javadoc documentation for details), which provides static utility methods to simplify some common tasks that are frequently used in RHS Java code. Adding an
import static gate.Utils.*; to the Imports block allows you to use these methods without
any prefix, for example:
1
{
AnnotationSet lookups = bindings . get ( " lookup " );
outputAS . add ( start ( lookups ) , end ( lookups ) , " Person " ,
featureMap ( " text " , stringFor ( doc , lookups )));
2
3
4
5
}
You can do the same with your own utility classes — JAPE rules can import any class
available to GATE, including classes defined in a plugin.
A JAPE file can optionally also contain Java code blocks for handling the events of when
the controller (pipeline) running the JAPE processing resource starts processing, finishes
processing, or processing is aborted (see the JavaDoc for ControllerAwarePR for more information and warnings about using this feature). These code blocks have to be defined after
any Import: block but before the first phase in the file using the ControllerStarted:,
ControllerFinished: and ControllerAborted: keywords:
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7
8
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10
Con trolle rStart ed : {
// code to run when the controller starts / before any transducing is done
}
C ont ro ll er Fi ni sh ed : {
// code to run right before the controller finishes / after all transducing
}
Con trolle rAbort ed : {
// code to run when processing is aborted by an exception or by a manual
// interruption
}
The Java code in each of these blocks can access the following predefined fields:
• controller: the Controller object running this JAPE transducer
• corpus: the Corpus object on which this JAPE transducer is run, if it is run by a
CorpusController, null otherwise.
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JAPE: Regular Expressions over Annotations
• ontology: the Ontology object if an Ontology LR has been specified as a runtimeparameter for this JAPE transducer, null otherwise
• ctx: the ActionContext object
• verb!throwable!: inside the ControllerAborted block, the Throwable which signalled
the aborting exception
8.7
Optimising for Speed
The way in which grammars are designed can have a huge impact on the processing speed.
Some simple tricks to keep the processing as fast as possible are:
• avoid the use of the * and + operators. Replace them with range queries where possible.
For example, instead of
({Token})*
use
({Token})[0,3]
if you can predict that you won’t need to recognise a string of Tokens longer than 3.
• avoid specifying unnecessary elements such as SpaceTokens where you can. To do this,
use the Input specification at the beginning of the grammar to stipulate the annotations
that need to be considered. If no Input specification is used, all annotations will be
considered (so, for example, you cannot match two tokens separated by a space unless
you specify the SpaceToken in the pattern). If, however, you specify Tokens but not
SpaceTokens in the Input, SpaceTokens do not have to be mentioned in the pattern
to be recognised. If, for example, there is only one rule in a phase that requires
SpaceTokens to be specified, it may be judicious to move that rule to a separate phase
where the SpaceToken can be specified as Input.
• avoid the shorthand syntax for copying feature values (newFeat = :bind.Type.oldFeat),
particularly if you need to copy multiple features from the left to the right hand side
of your rule.
8.8
Ontology Aware Grammar Transduction
GATE supports two different methods for ontology aware grammar transduction. Firstly it
is possible to use the ontology feature both in grammars and annotations, while using the
JAPE: Regular Expressions over Annotations
215
default transducer. Secondly it is possible to use an ontology aware transducer by passing
an ontology language resource to one of the subsumes methods in SimpleFeatureMapImpl.
This second strategy does not check for ontology features, which will make the writing
of grammars easier, as there is no need to specify ontology when writing them. More
information about the ontology-aware transducer can be found in Section 14.10.
8.9
Serializing JAPE Transducer
JAPE grammars are written as files with the extension ‘.jape’, which are parsed and compiled at run-time to execute them over the GATE document(s). Serialization of the JAPE
Transducer adds the capability to serialize such grammar files and use them later to bootstrap new JAPE transducers, where they do not need the original JAPE grammar file. This
allows people to distribute the serialized version of their grammars without disclosing the
actual contents of their jape files. This is implemented as part of the JAPE Transducer PR.
The following sections describe how to serialize and deserialize them.
8.9.1
How to Serialize?
Once an instance of a JAPE transducer is created, the option to serialize it appears in the
context menu of that instance. The context menu can be activated by right clicking on the
respective PR. Having done so, it asks for the file name where the serialized version of the
respective JAPE grammar is stored.
8.9.2
How to Use the Serialized Grammar File?
The JAPE Transducer now also has an init-time parameter binaryGrammarURL, which
appears as an optional parameter to the grammarURL. The User can use this parameter
(i.e. binaryGrammarURL) to specify the serialized grammar file.
8.10
Notes for Montreal Transducer Users
In June 2008, the standard JAPE transducer implementation gained a number of features
inspired by Luc Plamondon’s ‘Montreal Transducer’, which was available as a GATE plugin
for several years, and was made obsolete in Version 5.1. If you have existing Montreal Transducer grammars and want to update them to work with the standard JAPE implementation
you should be aware of the following differences in behaviour:
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JAPE: Regular Expressions over Annotations
• Quantifiers (*, + and ?) in the Montreal transducer are always greedy, but this is not
necessarily the case in standard JAPE.
• The Montreal Transducer defines {Type.feature != value} to be the same as
{!Type.feature == value} (and likewise the !~ operator in terms of =~). In standard JAPE these constructs have different semantics. {Type.feature != value}
will only match if there is a Type annotation whose feature feature does not
have the given value, and if it matches it will bind the single Type annotation.
{!Type.feature == value} will match if there is no Type annotation at a given place
with this feature (including when there is no Type annotation at all), and if it matches
it will bind every other annotation that starts at that location. If you have used !=
in your Montreal grammars and want them to continue to behave the same way you
must change them to use the prefix-! form instead (see Section 8.1.10).
• The =~ operator in standard JAPE looks for regular expression matches anywhere
within a feature value, whereas in the Montreal transducer it requires the whole string
to match. To obtain the whole-string matching behaviour in standard JAPE, use the
==~ operator instead (see Section 8.2.2).
Chapter 9
ANNIC: ANNotations-In-Context
ANNIC (ANNotations-In-Context) is a full-featured annotation indexing and retrieval system. It is provided as part of an extension of the Serial Data-stores, called Searchable Serial
Data-store (SSD).
ANNIC can index documents in any format supported by the GATE system (i.e., XML,
HTML, RTF, e-mail, text, etc). Compared with other such query systems, it has additional
features addressing issues such as extensive indexing of linguistic information associated with
document content, independent of document format. It also allows indexing and extraction of
information from overlapping annotations and features. Its advanced graphical user interface
provides a graphical view of annotation markups over the text, along with an ability to build
new queries interactively. In addition, ANNIC can be used as a first step in rule development
for NLP systems as it enables the discovery and testing of patterns in corpora.
ANNIC is built on top of the Apache Lucene1 – a high performance full-featured search engine
implemented in Java, which supports indexing and search of large document collections. Our
choice of IR engine is due to the customisability of Lucene. For more details on how Lucene
was modified to meet the requirements of indexing and querying annotations, please refer to
[Aswani et al. 05].
As explained earlier, SSD is an extension of the serial data-store. In addition to the persist
location, SSD asks user to provide some more information (explained later) that it uses to
index the documents. Once the SSD has been initiated, user can add/remove documents/corpora to the SSD in a similar way it is done with other data-stores. When documents are
added to the SSD, it automatically tries to index them. It updates the index whenever there
is a change in any of the documents stored in the SSD and removes the document from the
index if it is deleted from the SSD. Be warned that only the annotation sets, types and features initially provided during the SSD creation time, will be updated when adding/removing
documents to the datastore.
1
http://lucene.apache.org
217
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ANNIC: ANNotations-In-Context
SSD has an advanced graphical interface that allows users to issue queries over the SSD.
Below we explain the parameters required by SSD and how to instantiate it, how to use its
graphical interface and how to use SSD programmatically.
9.1
Instantiating SSD
Steps:
1. In GATE Developer, right click on ‘Datastores’ and select ‘Create Datastore’.
2. From a drop-down list select ‘Lucene Based Searchable DataStore’.
3. Here, you will see a file dialog. Please select an empty folder for your datastore. This
is similar to the procedure of creating a serial datastore.
4. After this, you will see an input window. Please provide these parameters:
(a) DataStore URL: This is the URL of the datastore folder selected in the previous
step.
(b) Index Location: By default, the location of index is calculated from the datastore
location. It is done by appending ‘-index’ to the datastore location. If user wants
to change this location, it is possible to do so by clicking on the folder icon and
selecting another empty folder. If the selected folder exists already, the system
will check if it is an empty folder. If the selected folder does not exist, the system
tries to create it.
(c) Annotation Sets: Here, you can provide one or more annotation sets that you
wish to index or exclude from being indexed. By default, the default annotation
set and the ‘Key’ annotation set are included. User can change this selection by
clicking on the edit list icon and removing or adding appropriate annotation set
names. In order to be able to readd the default annotation set, you must click on
the edit list icon and add an empty field to the list. If there are no annotation
sets provided, all the annotation sets in all documents are indexed.
(d) Base-Token Type: (e.g. Token or Key.Token) These are the basic tokens of any
document. Your documents must have the annotations of Base-Token-Type in
order to get indexed. These basic tokens are used for displaying contextual information while searching patterns in the corpus. In case of indexing more than
one annotation set, user can specify the annotation set from which the tokens
should be taken (e.g. Key.Token- annotations of type Token from the annotation
set called Key). In case user does not provide any annotation set name (e.g.
Token), the system searches in all the annotation sets to be indexed and the basetokens from the first annotation set with the base token annotations are taken.
Please note that the documents with no base-tokens are not indexed. However, if
ANNIC: ANNotations-In-Context
219
the ‘create tokens automatically’ option is selected, the SSD creates base-tokens
automatically. Here, each string delimited with white space is considered as a
token.
(e) Index Unit Type: (e.g. Sentence, Key.Sentence) This specifies the unit of Index.
In other words, annotations lying within the boundaries of these annotations are
indexed (e.g. in the case of ‘Sentences’, no annotations that are spanned across
the boundaries of two sentences are considered for indexing). User can specify
from which annotation set the index unit annotations should be considered. If
user does not provide any annotation set, the SSD searches among all annotation
sets for index units. If this field is left empty or SSD fails to locate index units,
the entire document is considered as a single unit.
(f) Features: Finally, users can specify the annotation types and features that should
be indexed or excluded from being indexed. (e.g. SpaceToken and Split). If user
wants to exclude only a specific feature of a specific annotation type, he/she can
specify it using a ’.’ separator between the annotation type and its feature (e.g.
Person.matches).
5. Click OK. If all parameters are OK, a new empty DS will be created.
6. Create an empty corpus and save it to the SSD.
7. Populate it with some documents. Each document added to the corpus and eventually
to the SSD is indexed automatically. If the document does not have the required
annotations, that document is skipped and not indexed.
SSDs are portable and can be moved across different systems. However, the relative positions
of both the datastore folder and the respective index folder must be maintained. If it is not
possible to maintain the relative positions, the new location of the index must be specified
inside the ‘ GATE SerialDataStore ’ file inside the datastore folder.
9.2
9.2.1
Search GUI
Overview
Figure 9.1 shows the search GUI for a datastore. The top section contains a text area to
write a query, lists to select the corpus and annotation set to search in, sliders to set the size
of the results and context and icons to execute and clear the query.
The central section shows a graphical visualisation of stacked annotations and feature values
for the result row selected in the bottom results table. You can also see the stack view
configuration window where you define which annotation type and feature to display in the
central section.
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ANNIC: ANNotations-In-Context
Figure 9.1: Searchable Serial Datastore Viewer.
The bottom section contains the results table of the query, i.e. the text that matches the
query with their left and right contexts. The bottom section contains also a tabbed pane of
statistics.
9.2.2
Syntax of Queries
SSD enables you to formulate versatile queries using a subset of JAPE patterns. Below, we
give the JAPE pattern clauses which can be used as SSD queries. Queries can also be a
combination of one or more of the following pattern clauses.
1. String
2. {AnnotationType}
3. {AnnotationType == String}
4. {AnnotationType.feature == feature value}
5. {AnnotationType1, AnnotationType2.feature == featureValue}
6. {AnnotationType1.feature == featureValue, AnnotationType2.feature == featureValue}
ANNIC: ANNotations-In-Context
221
Figure 9.2: Searchable Serial Datastore Viewer - Auto-completion.
JAPE patterns also support the | (OR) operator. For instance, {A} ({B} | {C}) is a pattern
of two annotations where the first is an annotation of type A followed by the annotation of
type either B or C.
ANNIC supports two operators, + and *, to specify the number of times a particular annotation or a sub pattern should appear in the main query pattern. Here, ({A})+n means one
and up to n occurrences of annotation {A} and ({A})*n means zero or up to n occurrences
of annotation {A}.
Below we explain the steps to search in SSD.
1. Double click on SSD. You will see an extra tab “Lucene DataStore Searcher”. Click
on it to activate the searcher GUI.
2. Here you can specify a query to search in your SSD. The query here is a L.H.S. part
of the JAPE grammar. Here are some examples:
(a) {Person} – This will return annotations of type Person from the SSD
(b) {Token.string == “Microsoft”} – This will return all occurrences of “Microsoft”
from the SSD.
(c) {Person}({Token})*2{Organization} – Person followed by zero or up to two tokens followed by Organization.
(d) {Token.orth==“upperInitial”, Organization} – Token with feature orth with
value set to “upperInitial” and which is also annotated as Organization.
9.2.3
Top Section
A text-area located in the top left part of the GUI is used to input a query. You can
copy/cut/paste with Control+C/X/V, undo/redo your changes with Control+Z/Y as usual.
To add a new line, use Control+Enter key combination.
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ANNIC: ANNotations-In-Context
Auto-completion as shown in figure 9.2 for annotation type is triggered when typing ’{’ or ’,’
and for feature when typing ’.’ after a valid annotation type. It shows only the annotation
types and features related to the selected corpus and annotation set.
If you right-click on an expression it will automatically select the shortest valid enclosing
brace and if you click on a selection it will propose you to add quantifiers for allowing the
expression to appear zero, one or more times.
To execute the query, click on the magnifying glass icon, use Enter key or Alt+Enter key
combination. To clear the query, click on the red X icon or use Alt+Backspace key combination.
It is possible to have more than one corpus, each containing a different set of documents,
stored in a single data-store. ANNIC, by providing a drop down box with a list of stored
corpora, also allows searching within a specific corpus. Similarly a document can have more
than one annotation set indexed and therefore ANNIC also provides a drop down box with
a list of indexed annotation sets for the selected corpus.
A large corpus can have many hits for a given query. This may take a long time to refresh
the GUI and may create inconvenience while browsing through results. Therefore you can
specify the number of results to retrieve. Use the Next Page of Results button to iterate
through results. Due to technical complexities, it is not possible to visit a previous page. To
retrieve all the results at the same time, push the results slider to the right end.
9.2.4
Central Section
Annotation types and features to show can be configured from the stack view configuration
window by clicking on the Configure button in the bottom left part of the central section.
You can also change the feature value to be displayed by double clicking on the annotation
type name.
The central section shows coloured rectangles exactly below the spans of text where these
annotations occur. If only an annotation type is displayed, the rectangle remains empty.
When you hover the mouse over the rectangle, it shows all their features and values in a
popup window. If an annotation type and a feature are displayed, the value of that feature
is shown in the rectangle.
Shortcuts are expression that stand for an ”AnnotationType.Feature” expression. For example, on the figure 9.1, the shortcut ”POS” stands for the expression ”Token.category”.
When you double click on an annotation rectangle, the respective query expression is placed
at the caret position in the query text area. If you have selected anything in the query text
area, it gets replaced. You can also double click on a word on the first line to add it to the
query.
ANNIC: ANNotations-In-Context
9.2.5
223
Bottom Section
The table of results contains the text matched by the query, the contexts, the features
displayed in the central view but only for the matching part, the effective query, the document
and annotation set names. You can sort a table column by clicking on its header.
You can remove a result from the results table or open the document containing it by rightclicking on a result in the results table.
ANNIC provides an Export button to export results into an HTML file. You can also select
then copy/paste the table in your word processor or spreadsheet.
A statistics tabbed pane is displayed on the bottom-right. There is always a global statistics
pane that list the count of the occurrences of all annotation types for the selected corpus
and annotation set.
Statistics can be obtained for matched spans of the query in the results, with or without
contexts, just by annotation type, an annotation type + feature or an annotation type +
feature + value. A second pane contains the one item statistics that you can add by rightclicking on a non empty annotation rectangle or on the header of a row in the central section.
You can sort a table column by clicking on its header.
9.3
Using SSD from GATE Embedded
9.3.1
How to instantiate a searchabledatastore
1
2
3
4
5
// create an instance of datastore
L u ce n e Da t a St o r eI m p l ds = ( L u ce n e Da t a St o r eI m p l )
Factory . createDataStore ( ‘ ‘ gate . persist . L uc e n e Da t a St o r eI m p l ’ ’ ,
dsLocation );
6
7
8
// we need to set Indexer
Indexer indexer = new LuceneIndexer ( new URL ( indexLocation ));
9
10
11
// set the parameters
Map parameters = new HashMap ();
12
13
14
// specify the index url
parameters . put ( Constants . INDEX_LOCATION_URL , new URL ( indexLocation ));
15
16
17
18
19
20
21
// specify the base token type
// and specify that the tokens should be created automatically
// if not found in the document
parameters . put ( Constants . BASE_TOKEN_ANNOTATION_TYPE , ‘‘ Token ’ ’ );
parameters . put ( Constants . CREATE_TOKENS_AUTOMATICALLY ,
new Boolean ( true ));
224
ANNIC: ANNotations-In-Context
22
23
24
// specify the index unit type
parameters . put ( Constants . INDEX_UNIT_ANNOTATION_TYPE , ‘‘ Sentence ’ ’ );
25
26
27
28
29
30
31
32
33
34
// specifying the annotation sets ”Key” and ”Default Annotation Set”
// to be indexed
List < String > setsToInclude = new ArrayList < String >();
setsToInclude . add ( " Key " );
setsToInclude . add ( " < null > " );
parameters . put ( Constants . ANNOTATION_SETS_NAMES_TO_INCLUDE ,
setsToInclude );
parameters . put ( Constants . ANNOTATION_SETS_NAMES_TO_EXCLUDE ,
new ArrayList < String >());
35
36
37
38
// all features should be indexed
parameters . put ( Constants . FEATURES_TO_INCLUDE , new ArrayList < String >());
parameters . put ( Constants . FEATURES_TO_EXCLUDE , new ArrayList < String >());
39
40
41
// set the indexer
ds . setIndexer ( indexer , parameters );
42
43
44
// set the searcher
ds . setSearcher ( new LuceneSearcher ());
9.3.2
How to search in this datastore
1
2
3
4
// obtain the searcher instance
Searcher searcher = ds . getSearcher ();
Map parameters = new HashMap ();
5
6
7
8
9
10
11
// obtain the url of index
String indexLocation =
new File ((( URL ) ds . getIndexer (). getParameters ()
. get ( Constants . IN DE X_ LO CA TI ON _U RL )). getFile ()). getAbsolutePath ();
ArrayList indexLocations = new ArrayList ();
indexLocations . add ( indexLocation );
12
13
14
// corpus2SearchIn = mention corpus name that was indexed here.
15
16
17
// the annotation set to search in
String a n n o t a t i o n S e t 2 S e a r c h I n = " Key " ;
18
19
20
21
22
23
24
// set the parameter
parameters . put ( Constants . INDEX_LOCATIONS , indexLocations );
parameters . put ( Constants . CORPUS_ID , corpus2SearchIn );
parameters . put ( Constants . ANNOTATION_SET_ID , annotationSet );
parameters . put ( Constants . CONTEXT_WINDOW , contextWindow );
parameters . put ( Constants . NO_OF_PATTERNS , noOfPatterns );
25
26
27
// search
String query = " { Person } " ;
ANNIC: ANNotations-In-Context
28
Hit [] hits = searcher . search ( query , parameters );
225
226
ANNIC: ANNotations-In-Context
Chapter 10
Performance Evaluation of Language
Analysers
When you can measure what you are speaking about, and express it in numbers,
you know something about it; but when you cannot measure it, when you cannot
express it in numbers, your knowledge is of a meager and unsatisfactory kind:
it may be the beginning of knowledge, but you have scarcely in your thoughts
advanced to the stage of science. (Kelvin)
Not everything that counts can be counted, and not everything that can be
counted counts. (Einstein)
GATE provides a variety of tools for automatic evaluation. The Annotation Diff tool compares two annotation sets within a document. Corpus QA extends Annotation Diff to an
entire corpus. The Corpus Benchmark tool also provides functionality for comparing annotation sets over an entire corpus. Additionally, two plugins cover similar functionality; one
implements inter-annotator agreement, and the other, the balanced distance metric.
These tools are particularly useful not just as a final measure of performance, but as a tool to
aid system development by tracking progress and evaluating the impact of changes as they
are made. Applications include evaluating the success of a machine learning or language
engineering application by comparing its results to a gold standard and also comparing
annotations prepared by two human annotators to each other to ensure that the annotations
are reliable.
This chapter begins by introducing the concepts and metrics relevant, before describing each
of the tools in turn.
227
228
10.1
Performance Evaluation of Language Analysers
Metrics for Evaluation in Information Extraction
When we evaluate the performance of a processing resource such as tokeniser, POS tagger,
or a whole application, we usually have a human-authored ‘gold standard’ against which to
compare our software. However, it is not always easy or obvious what this gold standard
should be, as different people may have different opinions about what is correct. Typically,
we solve this problem by using more than one human annotator, and comparing their annotations. We do this by calculating inter-annotator agreement (IAA), also known as inter-rater
reliability.
IAA can be used to assess how difficult a task is. This is based on the argument that if
two humans cannot come to agreement on some annotation, it is unlikely that a computer
could ever do the same annotation ‘correctly’. Thus, IAA can be used to find the ceiling for
computer performance.
There are many possible metrics for reporting IAA, such as Cohen’s Kappa, prevalence,
and bias [Eugenio & Glass 04]. Kappa is the best metric for IAA when all the annotators
have identical exhaustive sets of questions on which they might agree or disagree. In other
words, it is a classification task. This could be a task like ‘are these names male or female
names’. However, sometimes there is disagreement about the set of questions, e.g. when the
annotators themselves determine which text spans they ought to annotate, such as in named
entity extraction. That could be a task like ‘read over this text and mark up all references
to politics’. When annotators determine their own sets of questions, it is appropriate to
use precision, recall, and F-measure to report IAA. Precision, recall and F-measure are
also appropriate choices when assessing performance of an automated application against a
trusted gold standard.
In this section, we will first introduce some relevant terms, before outlining Cohen’s Kappa
and similar measures, in Section 10.1.2. We will then introduce precision, recall and Fmeasure in Section 10.1.3.
10.1.1
Annotation Relations
Before introducing the metrics we will use in this chapter, we will first outline the ways in
which annotations can relate to each other. These ways of comparing annotations to each
other are used to determine the counts that then go into calculating the metrics of interest.
Consider a document with two annotation sets upon it. These annotation sets might for
example be prepared by two human annotators, or alternatively, one set might be produced
by an automated system and the other might be a trusted gold standard. We wish to assess
the extent to which they agree. We begin by counting incidences of the following relations:
Coextensive Two annotations are coextensive if they hit the same span of text in a document. Basically, both their start and end offsets are equal.
Performance Evaluation of Language Analysers
229
Overlaps Two annotations overlap if they share a common span of text.
Compatible Two annotations are compatible if they are coextensive and if the features of
one (usually the ones from the key) are included in the features of the other (usually
the response).
Partially Compatible Two annotations are partially compatible if they overlap and if the
features of one (usually the ones from the key) are included in the features of the other
(response).
Missing This applies only to the key annotations. A key annotation is missing if either
it is not coextensive or overlapping, orif one or more features are not included in the
response annotation.
Spurious This applies only to the response annotations. A response annotation is spurious
if either it is not coextensive or overlapping, or if one or more features from the key
are not included in the response annotation.
10.1.2
Cohen’s Kappa
The three commonly used IAA measures are observed agreement, specific agreement, and
Kappa (κ) [Hripcsak & Heitjan 02]. Those measures can be calculated from a contingency
table, which lists the numbers of instances of agreement and disagreement between two
annotators on each category. To explain the IAA measures, a general contingency table for
two categories cat1 and cat2 is shown in Table 10.1.
Annotator-1
cat1
cat2
marginal sum
Annotator-2
cat1
cat2
a
b
c
d
a+c
b+d
marginal sum
a+b
c+d
a+b+c+d
Table 10.1: Contingency table for two-category problem
Observed agreement is the portion of the instances on which the annotators agree. For
the two annotators and two categories as shown in Table 10.1, it is defined as
Ao =
a+d
a+b+c+d
(10.1)
The extension of the above formula to more than two categories is straightforward. The
extension to more than two annotators is usually taken as the mean of the pair-wise agreements [Fleiss 75], which is the average agreement across all possible pairs of annotators. An
alternative compares each annotator with the majority opinion of the others [Fleiss 75].
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Performance Evaluation of Language Analysers
However, the observed agreement has two shortcomings. One is that a certain amount of
agreement is expected by chance. The Kappa measure is a chance-corrected agreement.
Another is that it sums up the agreement on all the categories, but the agreements on each
category may differ. Hence the category specific agreement is needed.
Specific agreement quantifies the degree of agreement for each of the categories separately.
For example, the specific agreement for the two categories list in Table 10.1 is the following,
respectively,
Acat1 =
2a
;
2a + b + c
Acat2 =
2d
b + c + 2d
(10.2)
Kappa is defined as the observed agreements Ao minus the agreement expected by chance
Ae and is normalized as a number between -1 and 1.
κ=
Ao − Ae
1 − Ae
(10.3)
κ = 1 means perfect agreements, κ = 0 means the agreement is equal to chance, κ = −1
means ‘perfect’ disagreement.
There are two different ways of computing the chance agreement Ae (for a detailed explanations about it see [Eugenio & Glass 04]; however, a quick outline will be given below). The
Cohen’s Kappa is based on the individual distribution of each annotator, while the Siegel
& Castellan’s Kappa is based on the assumption that all the annotators have the same
distribution. The former is more informative than the latter and has been used widely.
Let us consider an example:
Annotator-1
cat1
cat2
marginal sum
Annotator-2
cat1
cat2 marginal sum
1
2
3
3
4
7
4
6
10
Table 10.2: Example contingency table for two-category problem
Cohen’s Kappa requires that the expected agreement be calculated as follows. Divide
marginal sums by the total to get the portion of the instances that each annotator allocates
to each category. Multiply annotator’s proportions together to get the likelihood of chance
agreement, then total these figures. Table 10.3 gives a worked example.
The formula can easily be extended to more than two categories.
Siegel & Castellan’s Kappa is applicable for any number of annotators. Siegel & Castellan’s Kappa for two annotators is also known as Scott’s Pi (see [Lombard et al. 02]). It
Performance Evaluation of Language Analysers
cat1
cat2
Total
Annotator-1
3 / 10 = 0.3
7 / 10 = 0.7
Annotator 2
4 / 10 = 0.4
6 / 10 = 0.6
231
Multiplied
0.12
0.42
0.54
Table 10.3: Calculating Expected Agreement for Cohen’s Kappa
differs from Cohen’s Kappa only in how the expected agreement is calculated. Table 10.4
shows a worked example. Annotator totals are added together and divided by the number
of decisions to form joint proportions. These are then squared and totalled.
cat1
cat2
Total
Ann-1 Ann-2 Sum Joint Prop
3
4
7
7/20
7
6
13
13/20
JP-Squared
49/400=0.1225
169/400=0.4225
218/400 = 0.545
Table 10.4: Calculating Expected Agreement for Siegel & Castellan’s Kappa (Scott’s Pi)
The Kappa suffers from the prevalence problem which arises because imbalanced distribution of categories in the data increases Ae . The prevalence problem can be alleviated by
reporting the positive and negative specified agreement on each category besides the Kappa
[Hripcsak & Heitjan 02, Eugenio & Glass 04]. In addition, the so-called bias problem affects
the Cohen’s Kappa, but not S&C’s. The bias problem arises as one annotator prefers one
particular category more than another annotator. [Eugenio & Glass 04] advised to compute
the S&C’s Kappa and the specific agreements along with the Cohen’s Kappa in order to
handle these problems.
Despite the problem mentioned above, the Cohen’s Kappa remains a popular IAA measure.
Kappa can be used for more than two annotators based on pair-wise figures, e.g. the mean
of all the pair-wise Kappa as an overall Kappa measure. The Cohen’s Kappa can also be
extended to the case of more than two annotators by using the following single formula
[Davies & Fleiss 82]
IJ 2 − i c Yic2
κ=1−
P
P P
I(J(J − 1) c (pc (1 − pc )) + c j (pcj − pc )2 )
P P
(10.4)
Where I and J are the number of instances and annotators, respectively; Yic is the number
of annotators who assigns the category c to the instance I; pcj is the probability of the
annotator j assigning category c; pc is the probability of assigning category by all annotators
(i.e. averaging pcj over all annotators).
The Krippendorff’s alpha, another variant of Kappa, differs only slightly from the S&C’s
Kappa on nominal category problem (see [Carletta 96, Eugenio & Glass 04]).
However, note that the Kappa (and the observed agreement) is not applicable to some
tasks. Named entity annotation is one such task [Hripcsak & Rothschild 05]. In the named
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Performance Evaluation of Language Analysers
entity annotation task, annotators are given some text and are asked to annotate some
named entities (and possibly their categories) in the text. Different annotators may annotate
different instances of the named entity. So, if one annotator annotates one named entity in
the text but another annotator does not annotate it, then that named entity is a non-entity
for the latter. However, generally the non-entity in the text is not a well-defined term, e.g.
we don’t know how many words should be contained in the non-entity. On the other hand,
if we want to compute Kappa for named entity annotation, we need the non-entities. This
is why people don’t compute Kappa for the named entity task.
10.1.3
Precision, Recall, F-Measure
Much of the research in IE in the last decade has been connected with the MUC competitions, and so it is unsurprising that the MUC evaluation metrics of precision, recall
and F-measure [Chinchor 92] also tend to be used, along with slight variations. These
metrics have a very long-standing tradition in the field of IR [van Rijsbergen 79] (see also
[Manning & Schütze 99, Frakes & Baeza-Yates 92]).
Precision measures the number of correctly identified items as a percentage of the number
of items identified. In other words, it measures how many of the items that the system
identified were actually correct, regardless of whether it also failed to retrieve correct items.
The higher the precision, the better the system is at ensuring that what is identified is
correct.
Error rate is the inverse of precision, and measures the number of incorrectly identified
items as a percentage of the items identified. It is sometimes used as an alternative to
precision.
Recall measures the number of correctly identified items as a percentage of the total number
of correct items. In other words, it measures how many of the items that should have been
identified actually were identified, regardless of how many spurious identifications were made.
The higher the recall rate, the better the system is at not missing correct items.
Clearly, there must be a tradeoff between precision and recall, for a system can easily be
made to achieve 100% precision by identifying nothing (and so making no mistakes in what
it identifies), or 100% recall by identifying everything (and so not missing anything). The
F-measure [van Rijsbergen 79] is often used in conjunction with Precision and Recall, as a
weighted average of the two. False positives are a useful metric when dealing with a wide
variety of text types, because it is not dependent on relative document richness in the same
way that precision is. By this we mean the relative number of entities of each type to be
found in a set of documents.
When comparing different systems on the same document set, relative document richness
is unimportant, because it is equal for all systems. When comparing a single system’s
performance on different documents, however, it is much more crucial, because if a particular
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document type has a significantly different number of any type of entity, the results for that
entity type can become skewed. Compare the impact on precision of one error where the
total number of correct entities = 1, and one error where the total = 100. Assuming the
document length is the same, then the false positive score for each text, on the other hand,
should be identical.
Common metrics for evaluation of IE systems are defined as follows:
P recision =
Recall =
Correct + 1/2P artial
Correct + Spurious + P artial
Correct + 1/2P artial
Correct + M issing + P artial
F − measure =
(β 2 + 1)P ∗ R
(β 2 P ) + R
(10.5)
(10.6)
(10.7)
where β reflects the weighting of P vs. R. If β is set to 1, the two are weighted equally. With
β set to 0.5, precision weights twice as much as recall. And with β set to 2, recall weights
twice as much as precision.
F alseP ositive =
Spurious
c
(10.8)
where c is some constant independent from document richness, e.g. the number of tokens or
sentences in the document.
Note that we consider annotations to be partially correct if the entity type is correct and the
spans are overlapping but not identical. Partially correct responses are normally allocated a
half weight.
10.1.4
Macro and Micro Averaging
Where precision, recall and f-measure are calculated over a corpus, there are options in terms
of how document statistics are combined.
• Micro averaging essentially treats the corpus as one large document. Correct, spurious
and missing counts span the entire corpus, and precision, recall and f-measure are
calculated accordingly.
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• Macro averaging calculates precision, recall and f-measure on a per document basis,
and then averages the results.
The method of choice depends on the priorities of the case in question. Macro averaging
tends to increase the importance of shorter documents.
It is also possible to calculate a macro average across annotation types; that is to say,
precision, recall and f-measure are calculated separately for each annotation type and the
results then averaged.
10.2
The Annotation Diff Tool
The Annotation Diff tool enables two sets of annotations in one or two documents to be compared, in order either to compare a system-annotated text with a reference (hand-annotated)
text, or to compare the output of two different versions of the system (or two different systems). For each annotation type, figures are generated for precision, recall, F-measure. Each
of these can be calculated according to 3 different criteria - strict, lenient and average. The
reason for this is to deal with partially correct responses in different ways.
• The Strict measure considers all partially correct responses as incorrect (spurious).
• The Lenient measure considers all partially correct responses as correct.
• The Average measure allocates a half weight to partially correct responses (i.e. it takes
the average of strict and lenient).
It can be accessed both from GATE Developer and from GATE Embedded. Annotation Diff
compares sets of annotations with the same type. When performing the comparison, the
annotation offsets and their features will be taken into consideration. and after that, the
comparison process is triggered.
All annotations from the key set are compared with the ones from the response set, and those
found to have the same start and end offsets are displayed on the same line in the table.
Then, the Annotation Diff evaluates if the features of each annotation from the response set
subsume those features from the key set, as specified by the features names you provide.
To use the annotation diff tool, see Section 10.2.1. To create a gold standard, see section
10.2.2. To compare more than two annotation sets, see Section 3.4.3.
10.2.1
Performing Evaluation with the Annotation Diff Tool
The Annotation Diff tool is activated by selecting it from the Tools menu at the top of the
GATE Developer window. It will appear in a new window. Select the key and response
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Figure 10.1: Annotation diff window with the parameters at the top, the comparison table
in the center and the statistics panel at the bottom.
documents to be used (note that both must have been previously loaded into the system),
the annotation sets to be used for each, and the annotation type to be compared.
Note that the tool automatically intersects all the annotation types from the selected key
annotation set with all types from the response set.
On a separate note, you can perform a diff on the same document, between two different
annotation sets. One annotation set could contain the key type and another could contain
the response one.
After the type has been selected, the user is required to decide how the features will be
compared. It is important to know that the tool compares them by analysing if features
from the key set are contained in the response set. It checks for both the feature name and
feature value to be the same.
There are three basic options to select:
• To take ‘all’ the features from the key set into consideration
• To take only ‘some’ user selected features
• To take ‘none’ of the features from the key set.
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The weight for the F-Measure can also be changed - by default it is set to 1.0 (i.e. to
give precision and recall equal weight). Finally, click on ‘Compare’ to display the results.
Note that the window may need to be resized manually, by dragging the window edges as
appropriate).
In the main window, the key and response annotations will be displayed. They can be sorted
by any category by clicking on the central column header: ‘=?’. The key and response
annotations will be aligned if their indices are identical, and are color coded according to
the legend displayed at the bottom.
Precision, recall, F-measure are also displayed below the annotation tables, each according
to 3 criteria - strict, lenient and average. See Sections 10.2 and 10.1 for more details about
the evaluation metrics.
The results can be saves to an HTML file by using the ‘Export to HTML’ button. This
creates an HTML snapshot of what the Annotation Diff table shows at that moment. The
columns and rows in the table will be shown in the same order, and the hidden columns will
not appear in the HTML file. The colours will also be the same.
If you need more details or context you can use the button ‘Show document’ to display the
document and the annotations selected in the annotation diff drop down lists and table.
10.2.2
Creating a Gold Standard with the Annotation Diff Tool
In order to create a gold standard set from two sets you need to show the ‘Adjudication’
panel at the bottom. It will insert two checkboxes columns in the central table. Tick boxes
in the columns ‘K(ey)’ and ‘R(esponse)’ then input a Target set in the text field and use the
‘Copy selection to target’ button to copy all annotations selected to the target annotation
set.
There is a context menu for the checkboxes to tick them quickly.
Each time you will copy the selection to the target set to create the gold standard set, the
rows will be hidden in further comparisons. In this way, you will see only the annotations
that haven’t been processed. At the end of the gold standard creation you should have an
empty table.
To see again the copied rows, select the ‘Statistics’ tab at the bottom and use the button
‘Compare’.
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Figure 10.2: Annotation diff window with the parameters at the top, the comparison table
in the center and the adjudication panel at the bottom.
Figure 10.3: Corpus Quality Assurance showing the document statistics table
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10.3
Corpus Quality Assurance
10.3.1
Description of the interface
A bottom tab in each corpus view is entitled ‘Corpus Quality Assurance’. This tab will
allow you to calculate precision, recall and F-score between two annotation sets in a corpus
without the need to load a plugin. It extends the Annotation Diff functionality to the entire
corpus in a convenient interface.
The main part of the view consists of two tabs each containing a table. One tab is entitled
‘Corpus statistics’ and the other is entitled ‘Document statistics’.
To the right of the tabbed area is a configuration pane in which you can select the annotation
sets you wish to compare, the annotation types you are interested in and the annotation
features you wish to specify for use in the calculation if any.
You can also choose whether to calculate agreement on a strict or lenient basis or take the
average of the two. (Recall that strict matching requires two annotations to have an identical
span if they are to be considered a match, where lenient matching accepts a partial match;
annotations are overlapping but not identical in span.)
At the top, several icons are for opening a document (double-clicking on a row is also working)
or Annotation Diff only when a row in the document statistics table is selected, exporting the
tables to an HTML file, reloading the list of sets, types and features when some documents
have been modified in the corpus and getting this help page.
Corpus Quality Assurance works also with a corpus inside a datastore. Using a datastore is
useful to minimise memory consumption when you have a big corpus.
See the section 10.1 for more details about the evaluation metrics.
10.3.2
Step by step usage
Begin by selecting the annotation sets you wish to compare in the top list in the configuration
pane. Clicking on an annotation set labels it annotation set A for the Key (an ‘(A)’ will
appear beside it to indicate that this is your selection for annotation set A). Now click on
another annotation set. This will be labelled annotation set B for the response.
To change your selection, deselect an annotation set by clicking on it a second time. You can
now choose another annotation set. Note that you do not need to hold the control key down
to select the second annotation set. This list is configured to accept two (and no more than
two) selections. If you wish, you may check the box ‘present in every document’ to reduce
the annotation sets list to only those sets present in every document.
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You may now choose the annotation types you are interested in. If you don’t choose any
then all will be used. If you wish, you may check the box ‘present in every selected set’ to
reduce the annotation types list to only those present in every selected annotation set.
You can choose the annotation features you wish to include in the calculation. If you choose
features, then for an annotation to be considered a match to another, their feature values
must also match. If you select the box ‘present in every selected type’ the features list will
be reduced to only those present in every type you selected.
For the classification measures you must select only one type and one feature.
The ‘Measures’ list allows you to choose whether to calculate strict or lenient figures or
average the two. You may choose as many as you wish, and they will be included as columns
in the table to the left. The BDM measures allow to accept a match when the two concept
are close enough in an ontology even if their name are different. See section 10.6.
An ‘Options’ button above the ‘Measures’ list gives let you set some settings like the beta
for the Fscore or the BDM file.
Finally, click on the ‘Compare’ button to recalculate the tables. The figures that appear in
the several tables (one per tab) are described below.
10.3.3
Details of the Corpus statistics table
In this table you will see that one row appears for every annotation type you chose. Columns
give total counts for matching annotations (‘Match’ equivalent to TREC Correct), annotations only present in annotation set A/Key (‘Only A’ equivalent to TREC Missing), annotations only present in annotation set B/Response (‘Only B’ equivalent to TREC Spurious)
and annotations that overlapped (‘Overlap’ equivalent to TREC Partial).
Depending on whether one of your annotation sets is considered a gold standard, you might
prefer to think of ‘Only A’ as missing and ‘Only B’ as spurious, or vice versa, but the Corpus
Quality Assurance tool makes no assumptions about which if any annotation set is the gold
standard. Where it is being used to calculate Inter Annotator Agreement there is no concept
of a ‘correct’ set. However, in ‘MUC’ terms, ‘Match’ would be correct and ‘Overlap’ would
be partial.
After these columns, three columns appear for every measure you chose to calculate. If you
chose to calculate a strict F1, a recall, precision and F1 column will appear for the strict
counts. If you chose to calculate a lenient F1, precision, recall and F1 columns will also
appear for lenient counts.
In the corpus statistics table, calculations are done on a per type basis and include all
documents in the calculation. Final rows in the table provide summaries; total counts are
given along with a micro and a macro average.
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Micro averaging treats the entire corpus as one big document where macro averaging, on
this table, is the arithmetic mean of the per-type figures. See Section 10.1.4 for more detail
on the distinction between a micro and a macro average.
10.3.4
Details of the Document statistics table
In this table you will see that one row appears for every document in the corpus. Columns
give counts as in the corpus statistics table, but this time on a per-document basis.
As before, for every measure you choose to calculate, precision, recall and F1 columns will
appear in the table.
Summary rows, again, give a macro average (arithmetic mean of the per-document measures)
and micro average (identical to the figure in the corpus statistics table).
10.3.5
GATE Embedded API for the measures
You can get the same results as the Corpus Quality Assurance tool from your program by
using the classes that compute the results.
They are three for the moment: AnnotationDiffer, ClassificationMeasures and OntologyMeasures. All in gate.util package.
To compute the measures respect the order below.
Constructors and methods to initialise the measure objects:
AnnotationDiffer differ = new AnnotationDiffer();
differ.setSignificantFeaturesSet(Set<String> features);
ClassificationMeasures classificationMeasures = new ClassificationMeasures();
OntologyMeasures ontologyMeasures = new OntologyMeasures();
ontologyMeasures.setBdmFile(URL bdmFileUrl);
With bdmFileUrl an URL to a file of the format described at section 10.6.
Methods for computing the measures:
differ.calculateDiff(Collection key, Collection response)
classificationMeasures.calculateConfusionMatrix(AnnotationSet key,
AnnotationSet response, String type, String feature, boolean verbose)
ontologyMeasures.calculateBdm(Collection<AnnotationDiffer> differs)
With verbose to be set to true if you want to get printed the annotations ignored on the
”standard” output stream.
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Constructors, useful for micro average, no need to use calculateX methods as they must have
been already called:
AnnotationDiffer(Collection<AnnotationDiffer> differs)
ClassificationMeasures(Collection<ClassificationMeasures> tables)
OntologyMeasures(Collection<OntologyMeasures> measures)
Method for getting results for all 3 classes:
List<String> getMeasuresRow(Object[] measures, String title)
With measures an array of String with values to choose from:
• F1.0-score strict
• F1.0-score lenient
• F1.0-score average
• F1.0-score strict BDM
• F1.0-score lenient BDM
• F1.0-score average BDM
• Observed agreement
• Cohen’s Kappa
• Pi’s Kappa
Note that the numeric value ‘1.0’ represents the beta coefficient in the Fscore. See section 10.1
for more information on these measures.
Method only for ClassificationMeasures:
List<List<String>> getConfusionMatrix(String title)
The following example is taken from gate.gui.CorpusQualityAssurance#compareAnnotation
but hasn’t been ran so there could be some corrections to make.
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final int FSCORE_MEASURES = 0;
final int C L A S S I F I C A T I O N _ M E A S U R E S = 1;
ArrayList < String > documentNames = new ArrayList < String >();
TreeSet < String > types = new TreeSet < String >();
Set < String > features = new HashSet < String >();
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int measuresType = FSCORE_MEASURES ;
Object [] measures = new Object []
{ " F1 .0 - score strict " , " F0 .5 - score lenient BDM " };
String keySetName = " Key " ;
String responseSetName = " Response " ;
types . add ( " Person " );
features . add ( " gender " );
URL bdmFileUrl = null ;
try {
bdmFileUrl = new URL ( " file :/// tmp / bdm . txt " );
} catch ( M a l f o r m e d U R L E x c e p t i o n e ) {
e . printStackTrace ();
}
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boolean useBdm = false ;
for ( Object measure : measures ) {
if ((( String ) measure ). contains ( " BDM " )) { useBdm = true ; break ; }
}
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// for each document
for ( int row = 0; row < corpus . size (); row ++) {
boolean do cument WasLoa ded = corpus . isDocumentLoaded ( row );
Document document = ( Document ) corpus . get ( row );
documentNames . add ( document . getName ());
Set < Annotation > keys = new HashSet < Annotation >();
Set < Annotation > responses = new HashSet < Annotation >();
// get annotations from selected annotation sets
keys = document . getAnnotations ( keySetName );
responses = document . getAnnotations ( responseSetName );
if (! doc umentW asLoa ded ) { // in case of datastore
corpus . unloadDocument ( document );
Factory . deleteResource ( document );
}
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// fscore document table
if ( measuresType == FSCORE_MEASURES ) {
HashMap < String , AnnotationDiffer > differsByType =
new HashMap < String , AnnotationDiffer >();
AnnotationDiffer differ ;
Set < Annotation > keysIter = new HashSet < Annotation >();
Set < Annotation > responsesIter = new HashSet < Annotation >();
for ( String type : types ) {
if (! keys . isEmpty () && ! types . isEmpty ()) {
keysIter = (( AnnotationSet ) keys ). get ( type );
}
if (! responses . isEmpty () && ! types . isEmpty ()) {
responsesIter = (( AnnotationSet ) responses ). get ( type );
}
differ = new AnnotationDiffer ();
differ . s e t S i g n i f i c a n t F e a t u r e s S e t ( features );
differ . calculateDiff ( keysIter , responsesIter ); // compare
differsByType . put ( type , differ );
}
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d i f f e r s B y D o c T h e n T y p e . add ( differsByType );
differ = new AnnotationDiffer ( differsByType . values ());
List < String > measuresRow ;
if ( useBdm ) {
OntologyMeasures ontologyMeasures = new OntologyMeasures ();
ontologyMeasures . setBdmFile ( bdmFileUrl );
ontologyMeasures . calculateBdm ( differsByType . values ());
measuresRow = ontologyMeasures . getMeasuresRow (
measures , documentNames . get ( documentNames . size () -1));
} else {
measuresRow = differ . getMeasuresRow ( measures ,
documentNames . get ( documentNames . size () -1));
}
System . out . println ( Arrays . deepToString ( measuresRow . toArray ()));
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// classification document table
} else if ( measuresType == C L A S S I F I C A T I O N _ M E A S U R E S
&& ! keys . isEmpty () && ! responses . isEmpty ()) {
ClassificationMeasures classificationMeasures =
new C l a s s i f i c a t i o n M e a s u r e s ();
classificationMeasures . calculateConfusionMatrix (
( AnnotationSet ) keys , ( AnnotationSet ) responses ,
types . first () , features . iterator (). next () , false );
List < String > measuresRow = c l a s s i f i c a t i o n M e a s u r e s . getMeasuresRow (
measures , documentNames . get ( documentNames . size () -1));
System . out . println ( Arrays . deepToString ( measuresRow . toArray ()));
List < List < String > > matrix = c l a s s i f i c a t i o n M e a s u r e s
. ge tC on fu si on Ma tr ix ( documentNames . get ( documentNames . size () -1));
for ( List < String > matrixRow : matrix ) {
System . out . println ( Arrays . deepToString ( matrixRow . toArray ()));
}
}
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}
See method gate.gui.CorpusQualityAssurance#printSummary for micro and macro average
like in the Corpus Quality Assurance.
10.3.6
Quality Assurance PR
We have also implemented a processing resource called Quality Assurance PR that wraps the
functionality of the QA Tool. At the time of writing this documentation, the only difference
the QA PR has in terms of functionality is that the PR only accepts one measure at a time.
The Quality Assuarance PR is included in the Tools plugin. The PR can be added to any
existing corpus pipeline. Since the QA tool works on the entire corpus, the PR has to be
executed after all the documents in the corpus have been processed. In order to achieve this,
we have designed the PR in such a way that it only gets executed when the pipeline reaches
to the last document in the corpus. There are no init-time parameters but users are required
to provide values for the following run-time parameters.
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• annotationTypes - annotation types to compare.
• featuresNames - features of the annotation types (specified above) to compare.
• keyASName - the annotation set that acts as a gold standard set and contains annotations of the types specified above in the first parameter.
• responseASName - the annotation set that acts as a test set and contains annotations
of the types specified above in the first parameter.
• measure - one of the six pre-defined measures: F1 STRICT, F1 AVERAGE,
F1 LENIENT, F05 STRICT, F05 AVERAGE and F05 LENIENT.
• outputFolderUrl - the PR produces two html files in the folder mentioned in this
parameter. The files are document-stats.html and the corpus-stats.html. The former
lists statistics for each document and the latter lists statistics for each annotation type
in the corpus. In case of the document-stats.html, each document is linked with an
html file that contains the output of the annotation diff utility in GATE.
10.4
Corpus Benchmark Tool
Like the Corpus Quality Assurance functionality, the corpus benchmark tool enables evaluation to be carried out over a whole corpus rather than a single document. Unlike Corpus
QA, it uses matched corpora to achieve this, rather than comparing annotation sets within
a corpus. It enables tracking of the system’s performance over time. It provides more detailed information regarding the annotations that differ between versions of the corpus (e.g.
annotations created by different versions of an application) than the Corpus QA tool does.
The basic idea with the tool is to evaluate an application with respect to a ‘gold standard’.
You have a ‘marked’ corpus containing the gold standard reference annotations; you have
a ‘clean’ copy of the corpus that does not contain the annotations in question, and you
have an application that creates the annotations in question. Now you can see how you are
getting on, by comparing the result of running your application on ‘clean’ to the ‘marked’
annotations.
10.4.1
Preparing the Corpora for Use
You will need to prepare the following directory structure:
main directory (can have any name)
|
|__"clean" (directory containing unannotated documents in XML form)
|
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|__"marked" (directory containing annotated documents in XML form)
|
|__"processed" (directory containing the datastore which is generated
when you ‘store corpus for future evaluation’)
• main: you should have a main directory containing subdirectories for your matched
corpora. It does not matter what this directory is called. This is the directory you
will select when the program prompts, ‘Please select a directory which contains the
documents to be evaluated’.
• clean: Make a directory called ‘clean’ (case-sensitive), and in it, make a copy of your
corpus that does not contain the annotations that your application creates (though it
may contain other annotations). The corpus benchmark tool will apply your application to this corpus, so it is important that the annotations it creates are not already
present in the corpus. You can create this corpus by copying your ‘marked’ corpus and
deleting the annotations in question from it.
• marked: you should have a ‘gold standard’ copy of your corpus in a directory called
‘marked’ (case-sensitive), containing the annotations to which the program will compare those produced by your application. The idea of the corpus benchmark tool is to
tell you how good your application performance is relative to this annotation set. The
‘marked’ corpus should contain exactly the same documents as the ‘clean’ set.
• processed: this directory contains a third version of the corpus. This directory will
be created by the tool itself, when you run ‘store corpus for future evaluation’. We
will explain how to do this in Section 10.4.3
10.4.2
Defining Properties
The properties of the corpus benchmark tool are defined in the file ‘corpus tool.properties’,
which should be located in the GATE home directory. GATE will tell you where it’s looking
for the properties file in the ‘message’ panel when you run the Corpus Benchmark Tool. It
is important to prepare this file before attempting to run the tool because there is no file
present by default, so unless you prepare this file, the corpus benchmark tool will not work!
The following properties should be set:
• the precision/recall performance threshold for verbose mode, below which the annotation will be displayed in the results file. This enables problem annotations to be easily
identified. By default this is set to 0.5;
• the name of the annotation set containing the human-marked annotations (annotSetName);
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• the name of the annotation set containing the system-generated annotations (outputSetName);
• the annotation types to be considered (annotTypes);
• the feature values to be considered, if any (annotFeatures).
The default annotation set has to be represented by an empty string. The outputSetName
and annotSetName must be different, and cannot both be the default annotation set. (If
they are the same, then use the Annotation Set Transfer PR to change one of them.) If you
omit any line (or just leave the value blank), that property reverts to default. For example,
‘annotSetName=’ is the same as leaving that line out.
An example file is shown below:
threshold=0.7
annotSetName=Key
outputSetName=ANNIE
annotTypes=Person;Organization;Location;Date;Address;Money
annotFeatures=type;gender
Here is another example:
threshold=0.6
annotSetName=Filtered
outputSetName=
annotTypes=Mention
annotFeatures=class
10.4.3
Running the Tool
To use the tool, first make sure the properties of the tool have been set correctly (see Section
10.4.2 for how to do this) and that the corpora and directory structure have been prepared
as outlined in Section 10.4.1. Also, make sure that your application is saved to file (see
Section 3.8.3). Then, from the ‘Tools’ menu, select ‘Corpus Benchmark’. You have four
options:
1. Default Mode
2. Store Corpus for Future Evaluation
3. Human Marked Against Stored Processing Results
4. Human Marked Against Current Processing Results
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We will describe these options in a different order to that in which they appear on the menu,
to facilitate explanation.
Store Corpus for Future Evaluation populates the ‘processed’ directory with a datastore
containing the result of running your application on the ‘clean’ corpus. If a ‘processed’
directory exists, the results will be placed there; if not, one will be created. This creates
a record of the current application performance. You can rerun this operation any time to
update the stored set.
Human Marked Against Stored Processing Results compares the stored ‘processed’
set with the ‘marked’ set. This mode assumes you have already run ‘Store corpus for future
evaluation’. It performs a diff between the ‘marked’ directory and the ‘processed’ directory
and prints out the metrics.
Human Marked Against Current Processing Results compares the ‘marked’ set with
the result of running the application on the ‘clean’ corpus. It runs your application on the
documents in the ‘clean’ directory creating a temporary annotated corpus and performs a
diff with the documents in the ‘marked’ directory. After the metrics (recall, precision, etc.)
are calculated and printed out, it deletes the temporary corpus.
Default Mode runs ‘Human Marked Against Current Processing Results’ and ‘Human
Marked Against Stored Processing Results’ and compares the results of the two, showing
you where things have changed between versions. This is one of the main purposes of the
benchmark tool; to show the difference in performance between different versions of your
application.
Once the mode has been selected, the program prompts, ‘Please select a directory which
contains the documents to be evaluated’. Choose the main directory containing your corpus
directories. (Do not select ‘clean’, ‘marked’, or ‘processed’.) Then (except in ‘Human marked
against stored processing results’ mode) you will be prompted to select the file containing
your application (e.g. an .xgapp file).
The tool can be used either in verbose or non-verbose mode, by selecting or unselecting
the verbose option from the menu. In verbose mode, for any precision/recall figure below
the user’s pre-defined threshold (stored in corpus tool.properties file) the tool will show the
the non-coextensive annotations (and their corresponding text) for that entity type, thereby
enabling the user to see where problems are occurring.
10.4.4
The Results
Running the tool (either in ‘Human marked against stored processing results’, ‘Human
marked against current processing results’ or ‘Default’ mode) produces an HTML file, in
tabular form, which is output in the main GATE Developer messages window. This can
then be pasted into a text editor and viewed in a web browser for easier viewing. See figure
10.4 for an example.
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In each mode, the following statistics will be output:
1. Per-document figures, itemised by type: precision and recall, as well as detailed information about the differing annotations;
2. Summary by type (‘Statistics’): correct, partially correct, missing and spurious totals,
as well as whole corpus (micro-average) precision, recall and f-measure (F1), itemised
by type;
3. Overall average figures: precision, recall and F1 calculated as a macro-average (arithmetic average) of the individual document precisions and recalls.
In ‘Default’ mode, information is also provided about whether the figures have increased or
decreased in comparison with the ‘Marked’ corpus.
Figure 10.4: Fragment of results from corpus benchmark tool
10.5
A Plugin Computing Inter-Annotator Agreement
(IAA)
The interannotator agreement plugin, ‘Inter Annotator Agreement’, computes the Fmeasures, namely precision, recall and F1, suitable for named entity annotations (see Sec-
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tion 10.1.3), and agreement, Cohen’s kappa and Scott’s pi, suitable for text classification
tasks (see Section 10.1.2). In the latter case, a confusion matrix is also provided. In this
section we describe those measures and the output results from the plugin. But first we
explain how to load the plugin, and the input to and the parameters of the plugin.
First you need to load the plugin named ‘Inter Annotator Agreement’ into GATE Developer
using the tool Manage CREOLE Plugins, if it is not already loaded. Then you can create a
PR for the plugin from the ‘IAA Computation’ in the existing PR list. After that you can
put the PR into a Corpus Pipeline to use it.
The IAA Computation PR differs from the Corpus Benchmark Tool in the data preparation
required. As in the Corpus Benchmark Tool, the idea is to compare annotation sets, for
example, prepared by different annotators, but in the IAA Computation PR, these annotation sets should be on the same set of documents. Thus, one corpus is loaded into GATE
on which the PR is run. Different annotation sets contain the annotations which will be
compared. These should (obviously) have different names.
It falls to the user to decide whether to use annotation type or an annotation feature as
class; are two annotations considered to be in agreement because they have the same type
and the same span? Or do you want to mark up your data with an annotation type such
as ‘Mention’, thus defining the relevant annotations, then give it a ‘class’ feature, the value
of which should be matched in order that they are considered to agree? This is a matter of
convenience. For example, data from the Batch Learning PR (see Section 17.2) uses a single
annotation type and a class feature. In other contexts, using annotation type might feel
more natural; the annotation sets should agree about what is a ‘Person’, what is a ‘Date’
etc. It is also possible to mix the two, as you will see below.
The IAA plugin has two runtime parameters annSetsForIaa and annTypesAndFeats for
specifying the annotation sets and the annotation types and features, respectively. Values
should be separated by semicolons. For example, to specify annotation sets ‘Ann1’, ‘Ann2’
and ‘Ann3’ you should set the value of annSetsForIaa to ‘Ann1;Ann2;Ann3’. Note that more
than two annotation sets are possible. Specify the value of annTypesAndFeats as ‘Per’ to
compute the IAA for the three annotation sets on the annotation type Per. You can also
specify more than one annotation type and separate them by ‘;’ too, and optionally specify
an annotation feature for a type by attaching a ‘->’ followed by feature name to the end of
the annotation name. For example, ‘Per->label;Org’ specifies two annotation types Per and
Org and also a feature name label for the type Per. If you specify an annotation feature for
an annotation type, then two annotations of the same type will be regarded as being different
if they have different values of that feature, even if the two annotations occupy exactly the
same position in the document. On the other hand, if you do not specify any annotation
feature for an annotation type, then the two annotations of the type will be regarded as the
same if they occupy the same position in the document.
The parameter measureType specifies the type of measure computed. There are two
measure types; the F-measure (i.e. Precision, Recall and F1), and the observed agreement
and Cohen’s Kappa. For classification tasks such as document or sentence classification, the
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observed agreement and Cohen’s Kappa is often used, though the F-measure is applicable
too. In these tasks, the targets are already identified, and the task is merely to classify them
correctly. However, for the named entity recognition task, only the F-measure is applicable.
In such tasks, finding the ‘named entities’ (text to be annotated) is as much a part of the
task as correctly labelling it. Observed agreement and Cohen’s kappa are not suitable in this
case. See Section 10.1.2 for further discussion. The parameter has two values, FMEASURE
and AGREEMENTANDKAPPA. The default value of the parameter is FMEASURE.
Another parameter verbosity specifies the verbosity level of the plugin’s output. Level 2
displays the most detailed output, including the IAA measures on each document and the
macro-averaged results over all documents. Level 1 only displays the IAA measures averaged
over all documents. Level 0 does not have any output. The default value of the parameter
is 1. In the following we will explain the outputs in detail.
Yet another runtime parameter bdmScoreFile specifies the URL for a file containing the
BDM scores used for the BDM based IAA computation. The BDM score file should be
produced by the BDM computation plugin, which is described in Section 10.6. The BDMbased IAA computation will be explained below. If the parameter is not assigned any value,
or is assigned a file which is not a BDM score file, the PR will not compute the BDM based
IAA.
10.5.1
IAA for Classification
IAA has been used mainly in classification tasks, where two or more annotators are given
a set of instances and are asked to classify those instances into some pre-defined categories.
IAA measures the agreements among the annotators on the class labels assigned to the instances by the annotators. Text classification tasks include document classification, sentence
classification (e.g. opinionated sentence recognition), and token classification (e.g. POS tagging). The important point to note is that the evaluation set and gold standard set have
exactly the same instances, but some instances in the two sets have different class labels.
Identifying the instances is not part of the problem.
The three commonly used IAA measures are observed agreement, specific agreement, and
Kappa (κ) [Hripcsak & Heitjan 02]. See Section 10.1.2 for the detailed explanations of those
measures. If you select the value of the runtime parameter measureType as AGREEMENTANDKAPPA, the IAA plugin will compute and display those IAA measures for your classification task. Below, we will explain the output of the PR for the agreement and Kappa
measures.
At the verbosity level 2, the output of the plugin is the most detailed. It first prints out a list
of the names of the annotation sets used for IAA computation. In the rest of the results, the
first annotation set is denoted as annotator 0, and the second annotation set is denoted as
annotator 1, etc. Then the plugin outputs the IAA results for each document in the corpus.
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For each document, it displays one annotation type and optionally an annotation feature if
specified, and then the results for that type and that feature. Note that the IAA computations are based on the pairwise comparison of annotators. In other words, we compute
the IAA for each pair of annotators. The first results for one document and one annotation
type are the macro-averaged ones over all pairs of annotators, which have three numbers
for the three types of IAA measures, namely Observed agreement, Cohen’s kappa and Scott’s
pi. Then for each pair of annotators, it outputs the three types of measures, a confusion
matrix (or contingency table), and the specific agreements for each label. The labels are
obtained from the annotations of that particular type. For each annotation type, if a feature
is specified, then the labels are the values of that feature. Please note that two terms may
be added to the label list: one is the empty one obtained from those annotations which
have the annotation feature but do not have a value for the feature; the other is ‘Non-cat’,
corresponding to those annotations not having the feature at all. If no feature is specified,
then two labels are used: ‘Anns’ corresponding to the annotations of that type, and ‘Noncat’ corresponding to those annotations which are annotated by one annotator but are not
annotated by another annotator.
After displaying the results for each document, the plugin prints out the macro-averaged
results over all documents. First, for each annotation type, it prints out the results for each
pair of annotators, and the macro-averaged results over all pairs of annotators. Finally it
prints out the macro-averaged results over all pairs of annotators, all types and all documents.
Please note that the classification problem can be evaluated using the F-measure too. If you
want to evaluate a classification problem using the F-measure, you just need to set the run
time parameter measureType to FMEASURE.
10.5.2
IAA For Named Entity Annotation
The commonly used IAA measures, such as kappa, have not been used in text mark-up
tasks such as named entity recognition and information extraction, for reasons explained
in Section 10.1.2 (also see [Hripcsak & Rothschild 05]). Instead, the F-measures, such as
Precision, Recall, and F1, have been widely used in information extraction evaluations such
as MUC, ACE and TERN for measuring IAA. This is because the computation of the Fmeasures does not need to know the number of non-entity examples. Another reason is
that F-measures are commonly used for evaluating information extraction systems. Hence
IAA F-measures can be directly compared with results from other systems published in the
literature.
For computing F-measure between two annotation sets, one can use one annotation set as
gold standard and another set as system’s output and compute the F-measures such as
Precision, Recall and F1. One can switch the roles of the two annotation sets. The Precision
and Recall in the former case become Recall and Precision in the latter, respectively. But
the F1 remains the same in both cases. For more than two annotators, we first compute
F-measures between any two annotators and use the mean of the pair-wise F-measures as
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an overall measure.
The computation of the F-measures (e.g. Precision, Recall and F1) are shown in Section
10.1. As noted in [Hripcsak & Rothschild 05], the F1 computed for two annotators for one
specific category is equivalent to the positive specific agreement of the category.
The outputs of the IAA plugins for named entity annotation are similar to those for classification. But the outputs are the F-measures, such as Precision, Recall and F1, instead
of the agreements and Kappas. It first prints out the results for each document. For one
document, it prints out the results for each annotation type, macro-averaged over all pairs of
annotators, then the results for each pair of annotators. In the last part, the micro-averaged
results over all documents are displayed. Note that the results are reported in both the strict
measure and the lenient measure, as defined in Section 10.2.
Please note that, for computing the F-measures for the named entity annotations, the IAA
plugin carries out the same computation as the Corpus Benchmark tool. The IAA plugin is
simpler than the Corpus benchmark tool in the sense that the former needs only one set of
documents with two or more annotation sets, whereas the latter needs three sets of the same
documents, one without any annotation, another with one annotation set, and the third one
with another annotation set. Additionally, the IAA plugin can deal with more than two
annotation sets but the Corpus benchmark tool can only deal with two annotation sets.
10.5.3
The BDM-Based IAA Scores
For a named entity recognition system, if the named entity’s class labels are the names of
concepts in some ontology (e.g. in the ontology-based information extraction), the system
can be evaluated using the IAA measures based on the BDM scores. The BDM measures
the closeness of two concepts in an ontology. If an entity is identified but is assigned a label
which is close to but not the same as the true label, the system should obtain some credit
for it, which the BDM-based metric can do. In contrast, the conventional named entity
recognition measure does not take into account the closeness of two labels and does not give
any credit to one identified entity with a wrong label, regardless of how close the assigned
label is to the true label. For more explanation about BDM see Section 10.6.
In order to compute the BDM-based IAA, one has to assign the plugin’s runtime parameter
bdmScoreFile to the URL of a file containing the BDM scores. The file should be obtained
by using the BDM computation plugin, which is described in Section 10.6. Currently the
BDM-based IAA is only used for computing the F-measures for e.g. the entity recognition
problem. Please note that the F-measures can also be used for evaluation of classification
problem. The BDM is not used for computing other measures such as the observed agreement
and Kappa, though it is possible to implement it. Therefore currently one has to select
FMEASURE for the run time parameter measureType in order to use the BDM based IAA
computation.
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10.6
253
A Plugin Computing the BDM Scores for an Ontology
The BDM (balanced distance metric) measures the closeness of two concepts in an ontology
or taxonomy [Maynard 05, Maynard et al. 06]. It is a real number between 0 and 1. The
closer the two concepts are in an ontology, the greater their BDM score is. For detailed
explanation about the BDM, see the papers [Maynard 05, Maynard et al. 06]. The BDM can
be seen as an improved version of the learning accuracy [Cimiano et al. 03]. It is dependent
on the length of the shortest path connecting the two concepts and also the deepness of the
two concepts in ontology. It is also normalised with the size of ontology and also takes into
account the concept density of the area containing the two involved concepts.
The BDM has been used to evaluate the ontology based information extraction (qOBIE)
system [Maynard et al. 06]. The OBIE identifies the instances for the concepts of an ontology. It’s possible that an OBIE system identifies an instance successfully but does not
assign it the correct concept. Instead it assigns the instance a concept being close to the
correct one. For example, the entity ‘London’ is an instance of the concept Capital, and
an OBIE system assigns it the concept City which is close to the concept Capital in some
ontology. In that case the OBIE should obtain some credit according to the closeness of
the two concepts. That is where the BDM can be used. The BDM has also been used to
evaluate the hierarchical classification system [Li et al. 07b]. It can also be used for ontology
learning and alignment.
The BDM computation plugin computes BDM score for each pair of concepts in an ontology.
It has two run time parameters:
• ontologyURL – its value should be the URL of the ontology that one wants to
compute the BDM scores for.
• outputBDMFile – its value is the URL of a file which will store the BDM scores
computed.
The plugin has the name Ontology BDM Computation and the corresponding processing
resource’s name is BDM Computation PR. The PR can be put into a Pipeline. If it is put
into a Corpus Pipeline, the corpus used should contain at least one document.
The BDM computation used the formula given in [Maynard et al. 06]. The resulting file
specified by the runtime parameter outputBDMFile contains the BDM scores. It is a text
file. The first line of the file gives some meta information such as the name of ontology used
for BDM computation. From the second line of the file, each line corresponds to one pair of
concepts. One line is like
key=Service, response=Object, bdm=0.6617647, msca=Object, cp=1, dpk=1, dpr=0,
n0=2.0, n1=2.0, n2=2.8333333, bran=1.9565217
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It first shows the names of the two concepts (one as key and another as response, and the
BDM score, and then other parameters’ values used for the computation. Note that, since
the BDM is symmetric for the two concepts, the resulting file contains only one line for each
pair. So if you want to look for the BDM score for one pair of concepts, you can choose one
as key and another as response. If you cannot find the line for the pair, you have to change
the order of two concepts and retrieve the file again.
10.7
Quality Assurance Summariser for Teamware
When documents are annotated using Teamware, anonymous annotation sets are created
for the annotating annotators. This makes it impossible to run Quality Assurance on such
documents as annotation sets with same names in different documents may refer to the
annoations created by different annotators. This is specially the case when a requirement is
to compute Inter Annotator Agreement (IAA). The QA Summariser for Teamware PR
generates a summary of agreements among annotators. It does this by pairing individual
annotators involved in the annotation task. It also compares annotations of each individual
annotator with those available in the consensus annotation set in the respective documents.
The PR is available from the Teamware Tools plugin. It internally uses the QualityAssurancePR to calculate agreement statistics. User has to provide the following run-time
parameters:
• annotationTypes Annotation types for which the IAA has to be computed.
• featureNames Features of annotations that should be used in IAA computations.
If no value is provided, only annotation boundaries for same annotation types are
compared.
• measure one of the six pre-defined measures: F1 STRICT, F1 AVERAGE,
F1 LENIENT, F05 STRICT, F05 AVERAGE and F05 LENIENT.
• outputFolderUrl The PR produces a summary in this folder. More information on
the generated file is provided below.
The PR generates an index.html file in the output folder. This html file contains a table that
summarises the agreement statistics. Both the first row and the first column contain names
of annotators who were involved in the annotation task. For each pair of annotators who
did the annotations together on atleast one document, both the micro and macro averages
are produced.
Last two columns in each row give average macro and micro agreements of the respective
annotator with all the other annotators he or she did annotations together.
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These figures are color coded. The color green is used for a cell background to indicate
full agreement (i.e. 1.0). The background color becomes lighter as the agreement reduces
towards 0.5. At 0.5 agreement, the background color of a cell is fully white. From 0.5
downwards, the color red is used and as the agreement reduces further, the color becomes
darker with dark red at 0.0 agreement. Use of such a color coding makes it easy for user to
get an idea of how annotators are performing and locate specific pairs of annotations who
need more training or may be someone who deserves a pat on his/her back.
For each pair of annotators, the summary table provides a link (with caption document) to
another html document that summarises annotations of the two respective annotators on
per document basis. The details include number of annotations they agreed and disagreed
and the scores for recall, precision and f-measure. Each document name in this summary
is linked with another html document with indepth comparison of annotations. User can
actually see the annotations on which the annotators had agreed and disagreed.
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Chapter 11
Profiling Processing Resources
11.1
Overview
This is a reporting tool for GATE processing resources. It reports the total time taken by
processing resources and the time taken for each document to be processed by an application
of type corpus pipeline.
GATE use log4j, a logging system, to write profiling informations in a file. The GATE profiling reporting tool uses the file generated by log4j and produces a report on the processing
resources. It profiles JAPE grammars at the rule level, enabling the user precisely identify
the performance bottlenecks. It also produces a report on the time taken to process each
document to find problematic documents.
This initial code for the reporting tool was written by Intelius employees Andrew Borthwick
and Chirag Viradiya and generously released under the LGPL licence to be part of GATE.
Figure 11.1: Example of HTML profiling report for ANNIE
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11.1.1
Features
• Ability to generate the following two reports
– Report on processing resources. For each level of processing: application, processing resource (PR) and grammar rule, subtotalled at each level.
– Report on documents processed. For some or all PR, sorted in decreasing processing time.
• Report on processing resources specific features
– Sort order by time or by execution.
– Show or hide processing elements which took 0 milliseconds.
– Generate HTML report with a collapsible tree.
• Report on documents processed specific features
– Limit the number of document to show from the most time consuming.
– Filter the PR to display statistics for.
• Features common to both reports
– Generate report as indented text or in HTML format.
– Generate a report only on the log entries from the last logical run of GATE.
– All processing times are reported in milliseconds and in terms of percentage
(rounded to nearest 0.1%) of total time.
– Command line interface and API.
– Detect if the benchmark.txt file is modified while generating the report.
11.1.2
Limitations
Be aware that the profiling doesn’t support non corpus pipeline as application type. There
is indeed no interest in profiling a non corpus pipeline that works on one or no document at
all. To get meaningful results you should run your corpus pipeline on at least 10 documents.
11.2
Graphical User Interface
The activation of the profiling and the creation of profiling reports are accessible from the
‘Tools’ menu in GATE with the submenu ‘Profiling Reports’.
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You can ‘Start Profiling Applications’ and ‘Stop Profiling Applications’ at any time. The
logging is cumulative so if you want to get a new report you must use the ‘Clear Profiling
History’ menu item when the profiling is stopped.
Be very careful that you must start the profiling before you load your application or you will
need to reload every Processing Resource that uses a Transducer. Otherwise you will get an
Exception similar to:
java.lang.IndexOutOfBoundsException: Index: 2, Size: 0
at java.util.ArrayList.RangeCheck(ArrayList.java:547)
at java.util.ArrayList.get(ArrayList.java:322)
at gate.jape.SinglePhaseTransducer.updateRuleTime(SinglePhaseTransducer.java:678)
Two types of reports are available: ‘Report on Processing Resources’ and ‘Report on Documents Processed’. See the previous section for more information.
11.3
Command Line Interface
Report on processing resources Usage: java gate.util.reporting.PRTimeReporter [Options]
Options:
-i input file path (default: benchmark.txt in the user’s .gate directory1 )
-m print media - html/text (default: html)
-z suppressZeroTimeEntries - true/false (default: true)
-s sorting order - exec order/time taken (default: exec order)
-o output file path (default: report.html/txt in the system temporary directory)
-l logical start (not set by default)
-h show help
Note that suppressZeroTimeEntries will be ignored if the sorting order is ‘time taken’
Report on documents processed Usage: java gate.util.reporting.DocTimeReporter
[Options]
1
GATE versions up to 5.2 placed benchmark.txt in the execution directory.
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Options:
-i input file path (default: benchmark.txt in the user’s .gate directory2 )
-m print media - html/text (default: html)
-d number of docs, use -1 for all docs (default: 10 docs)
-p processing resource name to be matched (default: all prs)
-o output file path (default: report.html/txt in the system temporary directory)
-l logical start (not set by default)
-h show help
Examples
• Run report 1: Report on Total time taken by each processing element across corpus
– java -cp ”gate/bin:gate/lib/GnuGetOpt.jar” gate.util.reporting.PRTimeReporter
-i benchmark.txt -o report.txt -m text
• Run report 2: Report on Time taken by document within given corpus.
– java -cp ”gate/bin:gate/lib/GnuGetOpt.jar” gate.util.reporting.DocTimeReporter
-i benchmark.txt -o report.html -m html
11.4
Application Programming Interface
11.4.1
Log4j.properties
This is required to direct the profiling information to the benchmark.txt file. The benchmark.txt generated by GATE will be used as input for GATE profiling report tool as input.
• # File appender that outputs only benchmark messages
• log4j.appender.benchmarklog=org.apache.log4j.RollingFileAppender
• log4j.appender.benchmarklog.Threshold=DEBUG
• log4j.appender.benchmarklog.File=$user.home/.gate/benchmark.txt
2
GATE versions up to 5.2 placed benchmark.txt in the execution directory.
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• log4j.appender.benchmarklog.MaxFileSize=5MB
• log4j.appender.benchmarklog.MaxBackupIndex=1
• log4j.appender.benchmarklog.layout=org.apache.log4j.PatternLayout
• log4j.appender.benchmarklog.layout.ConversionPattern=%m%n
• # Configure the Benchmark logger so that it only goes to the benchmark log file
• log4j.logger.gate.util.Benchmark=DEBUG, benchmarklog
• log4j.additivity.gate.util.Benchmark=false
11.4.2
Benchmark log format
The format of the benchmark file that logs the times is as follow:
timestamp START PR_name
timestamp duration benchmarkID class features
timestamp duration benchmarkID class features
...
with the timestamp being the difference, measured in milliseconds, between the current time
and midnight, January 1, 1970 UTC.
Example:
1257269774770 START Sections_splitter
1257269774773 0 Sections_splitter.doc_EP-1026523-A1_xml_00008.documentLoaded
gate.creole.SerialAnalyserController
{corpusName=Corpus for EP-1026523-A1.xml_00008,
documentName=EP-1026523-A1.xml_00008}
...
11.4.3
Enabling profiling
There are two ways to enable profiling of the processing resources:
1. In gate/build.properties, add the line: run.gate.enable.benchmark=true
2. In your Java code, use the method: Benchmark.setBenchmarkingEnabled(true)
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11.4.4
Reporting tool
Report on processing resources
1. Instantiate the Class PRTimeReporter
(a) PRTimeReporter report = new PRTimeReporter();
2. Set the input benchmark file
(a) File benchmarkFile = new File(”benchmark.txt”);
(b) report.setBenchmarkFile(benchmarkFile);
3. Set the output report file
(a) File reportFile = new File(”report.txt”); or
(b) File reportFile = new File(”report.html”);
(c) report.setReportFile(reportFile);
4. Set the output format: in html or text format (default: MEDIA HTML)
(a) report.setPrintMedia(PRTimeReporter.MEDIA TEXT); or
(b) report.setPrintMedia(PRTimeReporter.MEDIA HTML);
5. Set the sorting order: Sort in order of execution or descending order of time taken
(default: EXEC ORDER)
(a) report.setSortOrder(PRTimeReporter.SORT TIME TAKEN); or
(b) report.setSortOrder(PRTimeReporter.SORT EXEC ORDER);
6. Set if suppress zero time entries: True/False (default: True). Parameter ignored if
SortOrder specified is ‘SORT TIME TAKEN’
(a) report.setSuppressZeroTimeEntries(true);
7. Set the logical start: A string indicating the logical start to be operated upon for
generating reports
(a) report.setLogicalStart(”InteliusPipelineStart”);
8. Generate the text/html report
(a) report.executeReport();
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Report on documents processed
1. Instantiate the Class DocTimeReporter
(a) DocTimeReporter report = new DocTimeReporter();
2. Set the input benchmark file
(a) File benchmarkFile = new File(”benchmark.txt”);
(b) report.setBenchmarkFile(benchmarkFile);
3. Set the output report file
(a) File reportFile = new File(”report.txt”); or
(b) File reportFile = new File(”report.html”);
(c) report.setReportFile(reportFile);
4. Set the output format: Generate report in html or text format (default: MEDIA HTML)
(a) report.setPrintMedia(DocTimeReporter.MEDIA TEXT); or
(b) report.setPrintMedia(DocTimeReporter.MEDIA HTML);
5. Set the maximum number of documents: Maximum number of documents to be displayed in the report (default: 10 docs)
(a) report.setNoOfDocs(2); // 2 docs or
(b) report.setNoOfDocs(DocTimeReporter.ALL DOCS); // All documents
6. Set the PR matching regular expression: A PR name or a regular expression to filter
the results (default: MATCH ALL PR REGEX).
(a) report.setSearchString(”HTML”); // match ALL PRS having HTML as substring
7. Set the logical start: A string indicating the logical start to be operated upon for
generating reports
(a) report.setLogicalStart(”InteliusPipelineStart”);
8. Generate the text/html report
(a) report.executeReport();
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Chapter 12
Developing GATE
This chapter describes ways of getting involved in and contributing to the GATE project.
Sections 12.1 and 12.2 are good places to start. Sections 12.3 and 12.4 describe protocol and
provide information for committers; we cover creating new plugins and updating this user
guide. See Section 12.2 for information on becoming a committer.
12.1
Reporting Bugs and Requesting Features
The GATE bug tracker can be found on SourceForge, here. When reporting bugs, please give
as much detail as possible. Include the GATE version number and build number, the platform
on which you observed the bug, and the version of Java you were using (1.5.0 15, 1.6.0 03,
etc.). Include steps to reproduce the problem, and a full stack trace of any exceptions,
including ‘Caused by . . . ’. You may wish to first check whether the bug is already fixed in
the latest nightly build. You may also request new features.
12.2
Contributing Patches
Patches may be submitted on SourceForge. The best format for patches is an SVN diff
against the latest subversion. The diff can be saved as a file and attached; it should not
be pasted into the bug report. Note that we generally do not accept patches against earlier
versions of GATE. Also, GATE is intended to be compatible with Java 6, so if you regularly
develop using a later version of Java it is very important to compile and test your patches
on Java 6. Patches that use features from a later version of Java and do not compile and
run on Java 6 will not be accepted.
If you intend to submit larger changes, you might prefer to become a committer! We welcome
input to the development process of GATE. The code is hosted on SourceForge, providing
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anonymous Subversion access (see Section 2.2.3). We’re happy to give committer privileges
to anyone with a track record of contributing good code to the project. We also make the
current version available nightly on the ftp site.
12.3
Creating New Plugins
GATE provides a flexible structure where new resources can be plugged in very easily. There
are three types of resources: Language Resource (LR), Processing Resource (PR) and Visual
Resource (VR). In the following subsections we describe the necessary steps to write new
PRs and VRs, and to add plugins to the nightly build. The guide on writing new LRs will
be available soon.
12.3.1
Where to Keep Plugins in the GATE Hierarchy
Each new resource added as a plugin should contain its own subfolder under the %GATEHOME%/plugins folder with an associated creole.xml file. A plugin can have one or more
resources declared in its creole.xml file and/or using source-level annotations as described in
section 4.7.
12.3.2
What to Call your Plugin
The plugins are many and the list is constantly expanding. The naming convention aims to
impose order and group plugins in a readable manner. When naming new plugins, please
adhere to the following guidelines:
• Words comprising plugin names should be capitalized and separated by underscores
Like So. This means that they will format nicely in GATE Developer. For example,
‘Inter Annotator Agreement’.
• Plugin names should begin with the word that best describes their function. Practically, this means that words are often reversed from the usual order, for example, the
Chemistry Tagger plugin should be called ‘Tagger Chemistry’. This means that for
example parsers will group together alphabetically and thus will be easy to find when
someone is looking for parsers. Before naming your plugin, look at the existing plugins
and see where it might group well.
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12.3.3
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Class Definition
Below we show a template class definition, which can be used in order to write a new
Processing Resource.
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package example ;
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import gate .*;
import gate . creole .*;
import gate . creole . metadata .*;
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/∗ ∗
∗ P r o c e s s i n g R e s o u r c e . The @ C r e o l e R e s o u r c e a n n o t a t i o n marks t h i s
∗
c l a s s a s a GATE R e s o u r c e , and g i v e s t h e i n f o r m a t i o n GATE n e e d s
∗ to configure the resource appropriately .
∗/
@CreoleResource ( name = " Example PR " ,
comment = " An example processing resource " )
public class NewPlugin extends A b s t r a c t L a n g u a g e A n a l y s e r {
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/∗
∗ t h i s method g e t s c a l l e d w h e n e v e r an o b j e c t o f t h i s
∗ c l a s s i s c r e a t e d e i t h e r from GATE D e v e l o p e r GUI o r i f
∗ i n i t i a t e d u s i n g F a c t o r y . c r e a t e R e s o u r c e ( ) method .
∗/
public Resource init () throws R e s o u r c e I n s t a n t i a t i o n E x c e p t i o n {
// here initialize all required variables, and may
// be throw an exception if the value for any of the
// mandatory parameters is not provided
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if ( this . rulesURL == null )
throw new R e s o u r c e I n s t a n t i a t i o n E x c e p t i o n ( " rules URL null " );
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return this ;
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}
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/∗
∗ t h i s method s h o u l d p r o v i d e t h e a c t u a l f u n c t i o n a l i t y o f t h e PR
∗ ( from w h e r e t h e main e x e c u t i o n b e g i n s ) . T h i s method
∗ g e t s c a l l e d when u s e r c l i c k on t h e ”RUN” b u t t o n i n t h e
∗ GATE D e v e l o p e r GUI ’ s a p p l i c a t i o n window .
∗/
public void execute () throws Ex ecu ti on Ex ce pt io n {
// write code here
}
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/∗ t h i s method i s c a l l e d t o r e i n i t i a l i z e t h e r e s o u r c e ∗/
public void reInit () throws R e s o u r c e I n s t a n t i a t i o n E x c e p t i o n {
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// reinitialization code
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}
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/∗
∗ There a r e two t y p e s o f p a r a m e t e r s
∗ 1 . I n i t time parameters − v a l u e s f o r t h e s e parameters need t o be
∗ p r o v i d e d a t t h e t i m e o f i n i t i a l i z i n g a new r e s o u r c e and t h e s e
∗ v a l u e s are not supposed to be changed .
∗ 2 . Runtime p a r a m e t e r s − v a l u e s f o r t h e s e p a r a m e t e r s a r e p r o v i d e d
∗ a t t h e t i m e o f e x e c u t i n g t h e PR . These a r e r u n t i m e p a r a m e t e r s and
∗ can b e c h a n g e d b e f o r e s t a r t i n g t h e e x e c u t i o n
∗ ( i . e . b e f o r e you c l i c k on t h e ”RUN” b u t t o n i n GATE D e v e l o p e r )
∗ A p a r a m e t e r myParam i s s p e c i f i e d b y a p a i r o f m e t h o d s getMyParam
∗ and setMyParam ( w i t h t h e f i r s t l e t t e r o f t h e p a r a m e t e r name
∗ c a p i t a l i z e d i n t h e n o r m a l J a v a Beans s t y l e ) , w i t h t h e s e t t e r
∗ annotated with a @CreoleParameter a n n o t a t i o n .
∗
∗ f o r example to s e t a v a l u e f o r outputAnnotationSetName
∗/
String o u t p u t A n n o t a t i o n S e t N a m e ;
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//getter and setter methods
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/∗ g e t <p a r a m e t e r name w i t h f i r s t l e t t e r C a p i t a l >
public String g e t O u t p u t A n n o t a t i o n S e t N a m e () {
return o u t p u t A n n o t a t i o n S e t N a m e ;
}
∗/
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/∗ The s e t t e r method i s a n n o t a t e d t o t e l l GATE t h a t i t d e f i n e s an
∗ o p t i o n a l runtime parameter .
∗/
@Optional
@RunTime
@CreoleParameter (
comment = " name of the annotationSet used for output " )
public void s e t O u p u t A n n o t a t i o n S e t N a m e ( String setName ) {
this . o u t p u t A n n o t a t i o n S e t N a m e = setName ;
}
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/∗ ∗ I n i t −t i m e p a r a m e t e r ∗/
URL rulesURL ;
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// getter and setter methods
public URL getRulesURL () {
return rulesFile ;
}
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/∗ T h i s p a r a m e t e r i s n o t a n n o t a t e d @RunTime o r @ O p t i o n a l , s o i t
∗ r e q u i r e d i n i t −t i m e p a r a m e t e r .
∗/
@CreoleParameter (
comment = " example of an inittime parameter " ,
defaultValue = " resources / morph / default . rul " )
is a
Developing GATE
public void setRulesURL ( URL rulesURL ) {
this . rulesURL = rulesURL ;
}
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}
PR Creole Entry
The creole.xml file simply needs to tell GATE which JAR file to look in to find the PR.
<?xml version="1.0"?>
<CREOLE-DIRECTORY>
<JAR SCAN="true">newplugin.jar</JAR>
</CREOLE-DIRECTORY>
Alternatively the configuration can be given in the XML file directly instead of using source
annotations. Section 4.7 gives the full details.
Context Menu
Each resource (LR,PR) has some predefined actions associated with it. These actions appear
in a context menu that appears in GATE Developer when the user right clicks on any of
the resources. For example if the selected resource is a Processing Resource, there will
be at least four actions available in its context menu: 1. Close 2. Hide 3. Rename and 4.
Reinitialize. New actions in addition to the predefined actions can be added by implementing
the gate.gui.ActionsPublisher interface in either the LR/PR itself or in any associated VR.
Then the user has to implement the following method.
public List getActions() {
return actions;
}
Here the variable actions should contain a list of instances of type javax.swing.AbstractAction.
A string passed in the constructor of an AbstractAction object appears in the context menu.
Adding a null element adds a separator in the menu.
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Listeners
There are at least four important listeners which should be implemented in order to listen
to the various relevant events happening in the background. These include:
• CreoleListener
Creole-register keeps information about instances of various resources and refreshes
itself on new additions and deletions. In order to listen to these events, a class should
implement the gate.event.CreoleListener. Implementing CreoleListener requires users
to implement the following methods:
– public void resourceLoaded(CreoleEvent creoleEvent);
– public void resourceUnloaded(CreoleEvent creoleEvent);
– public void resourceRenamed(Resource resource, String oldName, String newName);
– public void datastoreOpened(CreoleEvent creoleEvent);
– public void datastoreCreated(CreoleEvent creoleEvent);
– public void datastoreClosed(CreoleEvent creoleEvent);
• DocumentListener
A traditional GATE document contains text and a set of annotationSets. To get
notified about changes in any of these resources, a class should implement the
gate.event.DocumentListener. This requires users to implement the following methods:
– public void contentEdited(DocumentEvent event);
– public void annotationSetAdded(DocumentEvent event);
– public void annotationSetRemoved(DocumentEvent event);
• AnnotationSetListener
As the name suggests, AnnotationSet is a set of annotations.
To listen
to the addition and deletion of annotations, a class should implement the
gate.event.AnnotationSetListener and therefore the following methods:
– public void annotationAdded(AnnotationSetEvent event);
– public void annotationRemoved(AnnotationSetEvent event);
• AnnotationListener
Each annotation has a featureMap associated with it, which contains a set of feature
names and their respective values. To listen to the changes in annotation, one needs
to implement the gate.event.AnnotationListener and implement the following method:
– public void annotationUpdated(AnnotationEvent event);
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Figure 12.1: GATE GUI
12.3.4
Writing a New VR
Each resource (PR and LR) can have its own associated visual resource. When double
clicked, the resource’s respective visual resource appears in GATE Developer. The GATE
Developer GUI is divided into three visible parts (See Figure 12.1). One of them contains
a tree that shows the loaded instances of resources. The one below this is used for various
purposes - such as to display document features and that the execution is in progress. This
part of the GUI is referred to as ‘small’. The third and the largest part of the GUI is referred
to as ‘large’. One can specify which one of these two should be used for displaying a new
visual resource in the creole.xml.
Class Definition
Below we show a template class definition, which can be used in order to write a new Visual
Resource.
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package example . gui ;
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import gate .*;
import gate . creole .*;
import gate . creole . metadata .*;
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/∗
∗ An e x a m p l e V i s u a l R e s o u r c e f o r t h e New P l u g i n
∗ Note t h a t h e r e we e x t e n d s t h e A b s t r a c t V i s u a l R e s o u r c e c l a s s .
∗ The @ C r e o l e R e s o u r c e a n n o t a t i o n a s s o c i a t e s t h i s VR w i t h t h e
∗ u n d e r l y i n g PR t y p e i t d i s p l a y s .
∗/
@CreoleResource ( name = " Visual resource for new plugin " ,
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guiType = GuiType . LARGE ,
reso urceDi splaye d = " example . NewPlugin " ,
mainViewer = true )
public class NewPluginVR extends A b s t r a c t V i s u a l R e s o u r c e {
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/∗
∗ An I n i t method c a l l e d when t h e GUI i s
∗ the f i r s t time
∗/
public Resource init () {
// initialize GUI Components
return this ;
}
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initialized
for
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/∗
∗ Here t a r g e t i s t h e PR c l a s s t o w h i c h t h i s V i s u a l R e s o u r c e
∗ b e l o n g s . T h i s method i s c a l l e d a f t e r t h e i n i t ( ) method .
∗/
public void setTarget ( Object target ) {
// check if the target is an instance of what you expected
// and initialize local data structures if required
}
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}
Every document has its own document viewer associated with it. It comes with a single
component that shows the text of the original document. GATE provides a way to attach new
GUI plugins to the document viewer. For example AnnotationSet viewer, AnnotationList
viewer and Co-Reference editor. These are the examples of DocumentViewer plugins shipped
as part of the core GATE build. These plugins can be displayed either on the right or on
top of the document viewer. They can also replace the text viewer in the center (See figure
12.1). A separate button is added at the top of the document viewer which can be pressed
to display the GUI plugin.
Below we show a template class definition, which can be used to develop a new DocumentViewer plugin.
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/∗
∗ Note t h a t t h e c l a s s n e e d s t o e x t e n d s t h e A b s t r a c t D o c u m e n t V i e w c l a s s
∗/
@CreoleResource
public class D o c u m e n t V i e w e r P l u g i n extends A b s t r a c t D o c u m e n t V i e w {
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/∗ I m p l e m e n t e r s s h o u l d o v e r r i d e t h i s method and u s e i t
∗ p o p u l a t i n g t h e GUI .
∗/
public void initGUI () {
// write code to initialize GUI
}
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/∗ R e t u r n s t h e t y p e o f t h i s v i e w ∗/
public int getType () {
for
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// it can be any of the following constants
// from the gate.gui.docview.DocumentView
// CENTRAL, VERTICAL, HORIZONTAL
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}
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/∗ R e t u r n s t h e a c t u a l UI component t h i s v i e w r e p r e s e n t s . ∗/
public Component getGUI () {
// return the top level GUI component
}
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/∗ T h i s method c a l l e d w h e n e v e r v i e w b e c o m e s a c t i v e . ∗/
public void registerHooks () {
// register listeners
}
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/∗ T h i s method c a l l e d w h e n e v e r v i e w b e c o m e s i n a c t i v e . ∗/
public void unregisterHooks () {
// do nothing
}
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}
12.3.5
Adding Plugins to the Nightly Build
If you add a new plugin and want it to be part of the build process, you should create a
build.xml file with targets ‘build’, ‘test’, ‘distro.prepare’, ‘javadoc’ and ‘clean’. The build
target should build the JAR file, test should run any unit tests, distro.prepare should clean
up any intermediate files (e.g. the classes/ directory) and leave just what’s in Subversion,
plus the compiled JAR file and javadocs. The clean target should clean up everything,
including the compiled JAR and any generated sources, etc. You should also add your
plugin to ‘plugins.to.build’ in the top-level build.xml to include it in the build. This is by
design - not all the plugins have build files, and of the ones that do, not all are suitable for
inclusion in the nightly build (viz. SUPPLE, Section 16.3).
Note that if you are currently building gate by doing ‘ant jar’, be aware that this does not
build the plugins. Running just ‘ant’ or ‘ant all’ will do so.
12.4
Updating this User Guide
The GATE User Guide is maintained in the GATE subversion repository at SourceForge. If
you are a developer at Sheffield you do not need to check out the userguide explicitly, as it
will appear under the tao directory when you check out sale. For others, you can check it
out as follows:
svn checkout https://svn.sourceforge.net/svnroot/gate/userguide/trunk userguide
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The user guide is written in LATEX and translated to PDF using pdflatex and to HTML
using tex4ht. The main file that ties it all together is tao_main.tex, which defines the
various macros used in the rest of the guide and \inputs the other .tex files, one per
chapter.
12.4.1
Building the User Guide
You will need:
• A standard POSIX shell environment including GNU Make. On Windows this generally means Cygwin, on Mac OS X the XCode developer tools and on Unix the relevant
packages from your distribution.
• A copy of the userguide sources (see above).
• A LATEX installation, including pdflatex if you want to build the PDF version, and
tex4ht if you want to build the HTML. MiKTeX should work for Windows, texlive
(available in MacPorts) for Mac OS X, or your choice of package for Unix.
• The BibTeX database big.bib.
It must be located in the directory above
where you have checked out the userguide, i.e. if the guide sources are in
/home/bob/svn/userguide then big.bib needs to go in /home/bib/svn. Sheffield
developers will find that it is already in the right place, under sale, others will need
to download it from http://gate.ac.uk/sale/big.bib.
• The file http://gate.ac.uk/sale/utils.tex.
• A bit of luck.
Once these are all assembled it should be a case of running make to perform the actual build.
To build the PDF do make tao.pdf, for the one page HTML do make index.html and for
the several pages HTML do make split.html.
The PDF build generally works without problems, but the HTML build is known to hang
on some machines for no apparent reason. If this happens to you try again on a different
machine.
12.4.2
Making Changes to the User Guide
To make changes to the guide simply edit the relevant .tex files, make sure the guide still
builds (at least the PDF version), and check in your changes to the source files only.
Please do not check in your own built copy of the guide, the official user guide builds are
produced by a Hudson continuous integration server in Sheffield.
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If you add a section or subsection you should use the \sect or \subsect commands rather
than the normal LaTeX \section or \subsection. These shorthand commands take an
optional first parameter, which is the label to use for the section and should follow the
pattern of existing labels. The label is also set as an anchor in the HTML version of the
guide. For example a new section for the ‘Fish’ plugin would go in misc-creole.tex with
a heading of:
\sect[sec:misc-creole:fish]{The Fish Plugin}
and would have the persistent URL http://gate.ac.uk/userguide/sec:misc-creole:fish.
If your changes are to document a bug fix or a new (or removed) feature then you should
also add an entry to the change log in recent-changes.tex. You should include a reference
to the full documentation for your change, in the same way as the existing changelog entries
do. You should find yourself adding to the changelog every time except where you are just
tidying up or rewording existing documentation. Unlike in the other source files, if you add
a section or subsection you should use the \rcSect or \rcSubsect. Recent changes appear
both in the introduction and the appendix, so these commands enable nesting to be done
appropriately.
Section/subsection labels should comprise ‘sec’ followed by the chapter label and a descriptive
section identifier, each colon-separated. New chapter labels should begin ‘chap:’.
Try to avoid changing chapter/section/subsection labels where possible, as this may break
links to the section. If you need to change a label, add it in the file ‘sections.map’. Entries
in this file are formatted one per line, with the old section label followed by a tab followed
by the new section label.
The quote marks used should be ‘ and ’.
Titles should be in title case (capitalise the first word, nouns, pronouns, verbs, adverbs and
adjectives but not articles, conjunctions or prepositions). When referring to a numbered
chapter, section, subsection, figure or table, capitalise it, e.g. ‘Section 3.1’. When merely
using the words chapter, section, subsection, figure or table, e.g. ‘the next chapter’, do not
capitalise them. Proper nouns should be capitalised (‘Java’, ‘Groovy’), as should strings
where the capitalisation is significant, but not terms like ‘annotation set’ or ‘document’.
The user guide is rebuilt automatically whenever changes are checked in, so your change
should appear in the online version of the guide within 20 or 30 minutes.
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Part III
CREOLE Plugins
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Chapter 13
Gazetteers
...neurobiologists still go on openly studying reflexes and looking under the hood,
not huddling passively in the trenches. Many of them still keep wondering: how
does the inner life arise? Ever puzzled, they oscillate between two major fictions:
(1) The brain can be understood; (2) We will never come close. Meanwhile they
keep pursuing brain mechanisms, partly from habit, partly out of faith. Their
premise: The brain is the organ of the mind. Clearly, this three-pound lump of
tissue is the source of our ‘insight information’ about our very being. Somewhere
in it there might be a few hidden guidelines for better ways to lead our lives.
Zen and the Brain, James H. Austin, 1998 (p. 6).
13.1
Introduction to Gazetteers
A gazetteer consists of a set of lists containing names of entities such as cities, organisations,
days of the week, etc. These lists are used to find occurrences of these names in text, e.g.
for the task of named entity recognition. The word ‘gazetteer’ is often used interchangeably
for both the set of entity lists and for the processing resource that makes use of those lists
to find occurrences of the names in text.
When a gazetteer processing resource is run on a document, annotations of type Lookup are
created for each matching string in the text. Gazetteers usually do not depend on Tokens
or on any other annotation and instead find matches based on the textual content of the
document. (the Flexible Gazetteer, described in section 13.7, being the exception to
the rule). This means that an entry may span more than one word and may start or end
within a word. If a gazetteer that directly works on text does respect word boundaries, the
way how word boundaries are found might differ from the way the GATE tokeniser finds
word boundaries. A Lookup annotation will only be created if the entire gazetteer entry is
matched in the text. The details of how gazetteer entries match text depend on the gazetteer
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Gazetteers
processing resource and its parameters. In this chapter, we will cover several gazetteers.
13.2
ANNIE Gazetteer
The rest of this introductory section describes the ANNIE Gazetteer which is part of ANNIE
and also described in section 6.3. The ANNIE gazetteer is part of and proved by the ANNIE
plugin.
Each individual gazetteer list is a plain text file, with one entry per line.
Below is a section of the list for units of currency:
Ecu
European Currency Units
FFr
Fr
German mark
German marks
New Taiwan dollar
New Taiwan dollars
NT dollar
NT dollars
An index file (usually called lists.def) is used to describe all such gazetteer list files that
belong together. Each gazetteer list should reside in the same directory as the index file.
The gazetteer index files describes for each list the major type and optionally, a minor type
and a language, separated by colons. In the example below, the first column refers to the
list name, the second column to the major type, and the third to the minor type. These lists
are compiled into finite state machines. Any text strings matched by these machines will be
annotated with features specifying the major and minor types.
currency_prefix.lst:currency_unit:pre_amount
currency_unit.lst:currency_unit:post_amount
date.lst:date:specific_date
day.lst:date:day
monthen.lst:date:month:en
monthde.lst:date:month:de
season.lst:date:season
The major and minor type as well as the language will be added as features to only Lookup
annotation generated from a matching entry from the respective list. For example, if an entry
from the currency unit.lst gazetteer list matches some text in a document, the gazetteer
Gazetteers
281
processing resource will generate a Lookup annotation spanning the matching text and assign
the features major="currency unit" and minor="post amount" to that annotation.
Grammar rules (JAPE rules) can specify the types to be identified in particular circumstances. The major and minor types enable this identification to take place, by giving access
to items stored in particular lists or combinations of lists.
For example, if a day needs to be identified, the minor type ‘day’ would be specified in the
grammar, in order to match only information about specific days. If any kind of date needs
to be identified, the major type ‘date’ would be specified. This might include weeks, months,
years etc. as well as days of the week, and would give access to all the items stored in day.lst,
month.lst, season.lst, and date.lst in the example shown.
13.2.1
Creating and Modifying Gazetteer Lists
Gazetteer lists can be modified using any text editor or an editor inside GATE when you
double-click on the gazetteer in the resources tree. Use of an editor that can edit Unicode
UTF-8 files (e.g. the GATE Unicode editor) is advised, however, in order to ensure that the
lists are stored as UTF-8, which will minimise any language encoding problems, particularly
if e.g. accents, umlauts or characters from non-Latin scripts are present.
To create a new list, simply add an entry for that list to the definitions file and add the new
list in the same directory as the existing lists.
After any modifications have been made in an external editor, ensure that you reinitialise
the gazetteer PR in GATE, if one is already loaded, before rerunning your application.
13.2.2
ANNIE Gazetteer Editor
This editor is similar to GAZE editor but aims at being simpler by restricting itself to ANNIE
Gazetteers. To open it, double-click on the gazetteer in the resources tree.
It is composed of two tables:
• a left table with 4 columns (List name, Major, Minor, Language) for the index, usually
a .def file
• a right table with 1+2*n columns (Value, Feature 1, Value 1...Feature n, Value n) for
the lists, usually .lst files
When selecting a list in the left table you get its content displayed in the right table.
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Figure 13.1: ANNIE Gazetteer Editor
You can sort both tables by clicking on their column headers. A text field ‘Filter’ at the
bottom of the right table allows to display only the rows that contain the expression you
typed.
To edit a value in a table, double click on a cell or press F2 then press Enter when finished
editing the cell. To add a new row in both tables use the text field at the top and press
Enter or use the ‘New’ button next to it. When adding a new list you can select from the
list of existing gazetteer lists in the current directory or type a new file name. To delete a
row, press Shift+Delete or use the context menu. To delete more than one row select them
before.
You can reload a modified list by selecting it and right-clicking for the context menu item
‘Reload List’ or by pressing Control+R. When a list is modified its name in the left table is
coloured in red.
If you have set ‘gazetteerFeatureSeparator’ parameter then the right table will show a ‘Feature’ and ‘Value’ columns for each feature. To add a new couple of columns use the button
‘Add Cols’.
Note that in the left table, you can only select one row at a time.
The gazetteer like other language resource has a context menu in the resources tree to
‘Reinitialise’, ‘Save’ or ‘Save as...’ the resource.
The right table has a context menu for the current selection to help you creating new
gazetteer. It is similar with the actions found in a spreadsheet application like ‘Fill Down
Selection’, ‘Clear Selection’, ‘Copy Selection’, ‘Paste Selection’, etc.
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13.3
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Gazetteer Visual Resource - GAZE
Gaze is a tool for editing the gazetteer lists , definitions and mapping to ontology. It
is suitable for use both for Plain/Linear Gazetteers (Default and Hash Gazetteers) and
Ontology-enabled Gazetteers (OntoGazetteer). The Gazetteer PR associated with the viewer
is reinitialised every time a save operation is performed. Note that GAZE does not scale up
to very large lists (we suggest not using it to view over 40,000 entries and not to copy inside
more than 10, 000 entries).
Gaze is part of and provided by the ANNIE plugin. To make it possible to visualize gazetteers
with the Gaze visualizer, the ANNIE plugin must be loaded first. Double clicking on a
gazetteer PR that uses a gazetteer definition (index) file will display the contents of the
gazetteer in the main window. The first pane will display the definition file, while the right
pane will display whichever gazetteer list has been selected from it.
A gazetteer list can be modified simply by typing in it. it can be saved by clicking the Save
button. When a list is saved, the whole gazetteer is automatically reinitialised (and will be
ready for use in GATE immediately).
To edit the definition file, right click inside the pane and choose from the options (Inset, Edit,
Remove). A pop-up menu will appear to guide you through the remaining process. Save the
definition file by selecting Save. Again, the gazetteer will be reinitialised automatically.
13.3.1
Display Modes
The display mode depends on the type of gazetteer loaded in the VR. The mode in which
Linear/Plain Gazetteers are loaded is called Linear/Plain Mode. In this mode, the Linear
Definition is displayed in the left pane, and the Gazetteer List is displayed in the right pane.
The Ontology/Extended mode is on when the displayed gazetteer is ontology-aware, which
means that there exists a mapping between classes in the ontology and lists of phrases. Two
more panes are displayed when in this mode. On the top in the left-most pane there is a tree
view of the ontology hierarchy, and at the bottom the mapping definition is displayed. This
section describes the Linear/Plain display mode, the Ontology/Extended mode is described
in section 13.5.
Whenever a gazetteer PR that uses a gazetteer definition (index) file is loaded, the Gaze
gazetteer visualisation will appear on double-click over the gazetteer in the Processing Resources branch of the Resources Tree.
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13.3.2
Gazetteers
Linear Definition Pane
This pane displays the nodes of the linear definition, and allows manipulation of the whole
definition as a file, as well as the single nodes. Whenever a gazetteer list is modified, its
node in the linear definition is coloured in red.
13.3.3
Linear Definition Toolbar
All the functionality explained in this section (New, Load, Save, Save As) is accessible also
via File — Linear Definition in the menu bar of Gaze.
New – Pressing New invokes a file dialog where the location of the new definition is specified.
Load – Pressing Load invokes a file dialog, and after locating the new definition it is loaded
by pressing Open.
Save – Pressing Save saves the definition to the location from which it has been read.
Save As – Pressing Save As allows another location to be chosen, and the definition saved
there.
13.3.4
Operations on Linear Definition Nodes
Double-click node – Double-clicking on a definition node forces the displaying of the
gazetteer list of the node in the right-most pane of the viewer.
Insert – On right-click over a node and choosing Insert, a dialog is displayed, requesting
List, Major Type, Minor Type and Languages. The mandatory fields are List and Major
Type. After pressing OK, a new linear node is added to the definition.
Remove – On right-click over a node and choosing Remove, the selected linear node is
removed from the definition.
Edit – On right-click over a node and choosing Edit a dialog is displayed allowing changes
of the fields List, Major Type, Minor Type and Languages.
13.3.5
Gazetteer List Pane
The gazetteer list pane has a toolbar with similar to the linear definition’s buttons (New,
Load, Save, Save As). They work as predicted by their names and as explained in the Linear
Definition Pane section, and are also accessible from File / Gazetteer List in the menu bar
of Gaze. The only addition is Save All which saves all modified gazetteer lists. The editing
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of the gazetteer list is as simple as editing a text file. One could use Ctrl+A to select the
whole list, Ctrl+C to copy the selected, Ctrl+V to paste it, Del to delete the selected text
or a single character, etc.
13.3.6
Mapping Definition Pane
The mapping definition is displayed one mapping node per row. It consists of a gazetteer
list, ontology URL, and class id. The content of the gazetteer list in the node is accessible
through double-clicking. It is displayed in the Gazetteer List Pane. The toolbar allows the
creation of a new definition (New), the loading of an existing one (Load), saving to the same
or new location (Save/Save As). The functionality of the toolbar buttons is also available
via File.
13.4
OntoGazetteer
The Ontogazetteer, or Hierarchical Gazetteer, is a processing resource which can associate the
entities from a specific gazetteer list with a class in a GATE ontology language resource. The
OntoGazetteer assigns classes rather than major or minor types, and is aware of mappings
between lists and class IDs. The Gaze visual resource can display the lists, ontology mappings
and the class hierarchy of the ontology for a OntoGazetteer processing resource and provides
ways of editing these components.
13.5
Gaze Ontology Gazetteer Editor
This section describes the Gaze gazetteer editor when it displays an OntoGazetteer processing
resource. The editor consists of two parts: one for the editing of the lists and the mapping
of lists and one for editing the ontology. These two parts are described in the following
subsections.
13.5.1
The Gaze Gazetteer List and Mapping Editor
This is a VR for editing the gazetteer lists, and mapping them to classes in an ontology. It
provides load/store/edit for the lists, load/store/edit for the mapping information, loading
of ontologies, load/store/edit for the linear definition file, and mapping of the lists file to the
major type, minor type and language.
Left pane: A single ontology is visualized in the left pane of the VR. The mapping between
a list and a class is displayed by showing the list as a subclass with a different icon. The
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mapping is specified by drag and drop from the linear definition pane (in the middle) and/or
by right click menu.
Middle pane: The middle pane displays the nodes/lines in the linear definition file. By
double clicking on a node the corresponding list is opened. Editing of the line/node is done
by right clicking and choosing edit: a dialogue appears (lower part of the scheme) allowing
the modification of the members of the node.
Right pane: In the right pane a single gazetteer list is displayed. It can be edited and parts
of it can be cut/copied/pasted.
13.5.2
The Gaze Ontology Editor
Note: to edit ontologies within gate, the more recent ontology viewer editor provided by the
Ontology Tools which provides many more features can be used, see section 14.5.
This is a VR for editing the class hierarchy of an ontology. it provides storing to and loading
from RDF/RDFS, and provides load/edit/store of the class hierarchy of an ontology.
Left pane: The various ontologies loaded are listed here. On double click or right click and
edit from the menu the ontology is visualized in the Right pane.
Right pane: Besides the visualization of the class hierarchy of the ontology the following
operations are allowed:
• expanding/collapsing parts of the ontology
• adding a class in the hierarchy: by right clicking on the intended parent of the new
class and choosing add sub class.
• removing a class: via right clicking on the class and choosing remove.
As a result of this VR, the ontology definition file is affected/altered.
13.6
Hash Gazetteer
The Hash Gazetteer is a gazetteer implemented by the OntoText Lab (http://www.ontotext.com/).
Its implementation is based on simple lookup in several java.util.HashMap objects, and is
inspired by the strange idea of Atanas Kiryakov, that searching in HashMaps will be faster
than a search in a Finite State Machine (FSM). The Hash Gazetteer processing resource is
part of the ANNIE plugin.
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This gazetteer processing resource is implemented in the following way: Every phrase i.e.
every list entry is separated into several parts. The parts are determined by the whitespaces
lying among them. e.g. the phrase : ”form is emptiness” has three parts : “form”, “is”, and
“emptiness”. There is also a list of HashMaps: mapsList which has as many elements as
the longest (in terms of ‘count of parts’) phrase in the lists. So the first part of a phrase is
placed in the first map. The first part + space + second part is placed in the second map,
etc. The full phrase is placed in the appropriate map, and a reference to a Lookup object is
attached to it.
On first sight it seems that this algorithm is certainly much more memory-consuming than a
finite state machine (FSM) with the parts of the phrases as transitions, but this is actually not
so important since the average length of the phrases (in parts) in the lists is 1.1. On the other
hand, one advantage of the algorithm is that, although unconventional, on average it takes
four times less memory and works three times faster than an optimized FSM implementation.
13.6.1
Prerequisites
The phrases to be recognised should be listed in a set of files, one for each type of occurrence
(as for the standard gazetteer).
The gazetteer is built with the information from a file that contains the set of lists (which
are files as well) and the associated type for each list. The file defining the set of lists should
have the following syntax: each list definition should be written on its own line and should
contain:
• the file name (required)
• the major type (required)
• the minor type (optional)
• the language(s) (optional)
The elements of each definition are separated by ‘:’. The following is an example of a valid
definition:
personmale.lst:person:male:english
Each file named in the lists definition file is just a list containing one entry per line.
When this gazetteer is run over some input text (a GATE document) it will generate annotations of type Lookup having the attributes specified in the definition file.
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13.6.2
Parameters
The Hash Gazetteer processing resource allows the specification of the following parameters
when it is created:
caseSensitive: this can be switched between true and false to indicate if matches should
be done in a case-sensitive way.
encoding: the encoding of the gazetteer lists
listsURL: the URL of the list definitions (index) file, i.e. the file that contains the filenames,
major types and optionally minor types and languages of all the list files.
There is one run-time parameter, annotationSetName that allows the specification of the
annotation set in which the Lookup annotations will be created. If nothing is specified the
default annotation set will be used.
13.7
Flexible Gazetteer
The Flexible Gazetteer provides users with the flexibility to choose their own customized
input and an external Gazetteer. For example, the user might want to replace words in the
text with their base forms (which is an output of the Morphological Analyser) or to segment
a Chinese text (using the Chinese Tokeniser) before running the Gazetteer on the Chinese
text.
The Flexible Gazetteer performs lookup over a document based on the values of an arbitrary
feature of an arbitrary annotation type, by using an externally provided gazetteer. It is
important to use an external gazetteer as this allows the use of any type of gazetteer (e.g.
an Ontological gazetteer).
Input to the Flexible Gazetteer:
Runtime parameters:
• Document – the document to be processed
• inputAnnotationSetName The annotationSet where the Flexible Gazetteer should
search for the AnnotationType.feature specified in the inputFeatureNames.
• outputAnnotationSetName The AnnotationSet where Lookup annotations should
be placed.
Creation time parameters:
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• inputFeatureNames – when selected, these feature values are used to replace the
corresponding original text. A temporary document is created from the values of the
specified features on the specified annotation types. For example: for Token.string
the temporary document will have the same content as the original one but all the
SpaceToken annotations will have been replaced by single spaces.
• gazetteerInst – the actual gazetteer instance, which should run over a temporary
document. This generates the Lookup annotations with features. This must be an
instance of gate.creole.gazetteer.Gazetteer which has already been created. All
such instances will be shown in the dropdown menu for this parameter in GATE
Developer.
Once the external gazetteer has annotated text with Lookup annotations, Lookup annotations on the temporary document are converted to Lookup annotations on the original
document. Finally the temporary document is deleted.
13.8
Gazetteer List Collector
The gazetteer list collector collects occurrences of entities directly from a set of annotated
training texts, and populates gazetteer lists with the entities. The entity types and structure
of the gazetteer lists are defined as necessary by the user. Once the lists have been collected,
a semantic grammar can be used to find the same entities in new texts.
An empty list must be created first for each annotation type, if no list exists already. The
set of lists must be loaded into GATE before the PR can be run. If a list already exists, the
list will simply be augmented with any new entries. The list collector will only collect one
occurrence of each entry: it first checks that the entry is not present already before adding
a new one.
There are 4 runtime parameters:
• annotationTypes: a list of the annotation types that should be collected
• gazetteer: the gazetteer where the results will be stored (this must be already loaded
in GATE)
• markupASname: the annotation set from which the annotation types should be collected
• theLanguage: sets the language feature of the gazetteer lists to be created to the
appropriate language (in the case where lists are collected for different languages)
Figure 13.2 shows a screenshot of a set of lists collected automatically for the Hindi language.
It contains 4 lists: Person, Organisation, Location and a list of stopwords. Each list has a
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Gazetteers
majorType whose value is the type of list, a minorType ‘inferred’ (since the lists have been
inferred from the text), and the language ‘Hindi’.
Figure 13.2: Lists collected automatically for Hindi
The list collector also has a facility to split the Person names that it collects into their
individual tokens, so that it adds both the entire name to the list, and adds each of the
tokens to the list (i.e. each of the first names, and the surname) as a separate entry. When
the grammar annotates Persons, it can require them to be at least 2 tokens or 2 consecutive Person Lookups. In this way, new Person names can be recognised by combining
a known first name with a known surname, even if they were not in the training corpus.
Where only a single token is found that matches, an Unknown entity is generated, which
can later be matched with an existing longer name via the orthomatcher component which
performs orthographic coreference between named entities. This same procedure can also
be used for other entity types. For example, parts of Organisation names can be combined
together in different ways. The facility for splitting Person names is hardcoded in the file
gate/src/gate/creole/GazetteerListsCollector.java and is commented.
13.9
OntoRoot Gazetteer
OntoRoot Gazetteer is a type of a dynamically created gazetteer that is, in combination
with few other generic GATE resources, capable of producing ontology-based annotations
over the given content with regards to the given ontology. This gazetteer is a part of
‘Gazetteer Ontology Based’ plugin that has been developed as a part of the TAO project.
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13.9.1
291
How Does it Work?
To produce ontology-based annotations i.e. annotations that link to the specific concepts
or relations from the ontology, it is essential to pre-process the Ontology Resources (e.g.,
Classes, Instances, Properties) and extract their human-understandable lexicalisations.
As a precondition for extracting human-understandable content from the ontology, first a
list of the following is being created:
• names of all ontology resources i.e. fragment identifiers
1
and
• assigned property values for all ontology resources (e.g., label and datatype property
values)
Each item from the list is further processed so that:
• any name containing dash ("-") or underline ("_") character(s) is processed so that
each of these characters is replaced by a blank space. For example, Project_Name or
Project-Name would become a Project Name.
• any name that is written in camelCase style is actually split into its constituent words,
so that ProjectName becomes a Project Name (optional).
• any name that is a compound name such as ‘POS Tagger for Spanish’ is split so that
both ‘POS Tagger’ and ‘Tagger’ are added to the list for processing. In this example,
‘for’ is a stop word, and any words after it are ignored (optional).
Each item from this list is analysed separately by the Onto Root Application (ORA) on
execution (see figure 13.3). The Onto Root Application first tokenises each linguistic term,
then assigns part-of-speech and lemma information to each token.
As a result of that pre-processing, each token in the terms will have additional feature named
‘root’, which contains the lemma as created by the morphological analyser. It is this lemma
or a set of lemmas which are then added to the dynamic gazetteer list, created from the
ontology.
For instance, if there is a resource with a short name (i.e., fragment identifier) ProjectName,
without any assigned properties the created list before executing the OntoRoot gazetteer
collection will contain the following strings:
• ‘ProjectName’,
1
An ontology resource is usually identified by an URI concatenated with a set of characters starting with
‘#’. This set of characters is called fragment identifier. For example, if the URI of a class representing
GATE POS Tagger is: ’http://gate.ac.uk/ns/gate-ontology#POSTagger’, the fragment identifier will be
’POSTagger’.
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Figure 13.3: Building Ontology Resource Root (OntoRoot) Gazetteer from the Ontology
• ‘Project Name’ after separating camelCased word and
• ‘Name’ after applying heuristic rules.
Each of the item from the list is then analysed separately and the results would be the same
as the input strings, as all of entries are nouns given in singular form.
13.9.2
Initialisation of OntoRoot Gazetteer
To initialise the gazetteer there are few mandatory parameters:
• Ontology to be processed;
• Tokeniser, POS Tagger and GATE Morphological Analyser to be used during processing.
and few optional ones:
• useResourceUri, default is set to true - should this gazetteer analyse resource URIs or
not;
• considerProperties, default is set to true - should this gazetteer consider properties or
not;
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• propertiesToInclude - checked only if considerProperties is set to true - this parameter
contains the list of property names (URIs) to be included, comma separated;
• propertiesToExclude - checked only if considerProperties is set to true - this parameter
contains the list of property names to be excluded, comma separated;
• caseSensitive, default set to be false -should this gazetteer differentiate on case;
• separateCamelCasedWords, default set to true - should this gazetteer separate emphcamelCased words, e.g. ‘ProjectName’ into ‘Project Name’;
• considerHeuristicRules, default set to false - should this gazetteer consider several
heuristic rules or not. Rules include splitting the words containing spaces, and using
prepositions as stop words; for example, if ’pos tagger for Spanish’ would be analysed,
‘for’ would be considered as a stop word; heuristically derived would be ‘pos tagger’
and this would be further used to add ‘pos tagger’ to the gazetteer list, with a feature
emphheuristical level set to be 0, and ‘tagger’ with emphheuristical level 1; at runtime
lower heuristical level should be preferred. NOTE: setting considerHeuristicRules to
true can cause a lot of noise for some ontologies and is likely to require implementing
an additional filtering resource that will prefer the annotations with the lower heuristic
level;
13.9.3
Simple steps to run OntoRoot Gazetteer
OntoRoot Gazetteer is a part of the Gazetteer Ontology Based plugin.
Easy way
For a quick start with the OntoRoot Gazetteer, consider running it from the GATE Developer
(GATE GUI):
• Start GATE
• Load a sample application from resources folder (exampleApp.xgapp). This will load
CAT App application.
• Run CAT App application and open query-doc to see a set of Lookup annotations
generated as a result (see Figure 13.4).
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Gazetteers
Figure 13.4: Sample ontology-based annotation as a result of running OntoRoot Gazetteer.
Feature URI refers to the URI of the ontology resource, while type identifies the type of the
resource such as class, instance, property, or datatypePropertyValue
Hard way
OntoRoot Gazetteer can easily be set up to be used with any ontology. To generate a GATE
application which demonstrates the use of the OntoRoot Gazetteer, follow these steps:
1. Start GATE
2. Load necessary plugins: Click on Manage CREOLE plugins and check the following:
• Tools
• Ontology
• Ontology Based Gazetteer
• Ontology Tools (optional); this parameter is required in order to view ontology
using the GATE Ontology Editor.
• ANNIE.
Make sure that these plugins are loaded from GATE/plugins/[plugin name] folder.
3. Load an ontology. Right click on Language Resource, and select the last option
to create an OWLIM Ontology LR. Specify the format of the ontology, for example rdfXmlURL, and give the correct path to the ontology: either the absolute path on your local machine such as c:/myOntology.owl or the URL such as
http://gate.ac.uk/ns/gate-ontology. Specify the name such as myOntology (this
is optional).
4. Create Processing Resources: Right click on the Processing Resource and create the
following PRs (with default parameters):
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• Document Reset PR
• ANNIE English Tokeniser
• ANNIE POS Tagger
• GATE Morphological Analyser
• RegEx Sentence Splitter (or ANNIE Sentence Splitter)
5. Create an Onto Root Gazetteer and set the init parameters. Mandatory ones are:
• Ontology: select previously created myOntology;
• Tokeniser : select previously created Tokeniser;
• POS Tagger : select previously created POS Tagger;
• Morpher : select previously created Morpher.
OntoRoot gazetteer is quite flexible in that it can be configured using the optional
parameters. List of all parameters is detailed in Section 13.9.2.
When all parameters are set click OK. It can take some time to initialise OntoRoot Gazetteer. For example, loading GATE knowledge base from
http://gate.ac.uk/ns/gate-kb takes around 6-15 seconds. Larger ontologies can
take much longer.
6. Create another PR which is a Flexible Gazetteer. As init parameters it is mandatory to
select previously created OntoRoot Gazetteer for gazetteerInst. For another parameter,
inputFeatureNames, click on the button on the right and when prompt with a window,
add ’Token.root’ in the provided textbox, then click Add button. Click OK, give name
to the new PR (optional) and then click OK.
7. Create an application. Right click on Application, then New Pipeline (or Corpus
Pipeline). Add the following PRs to the application in this particular order:
• Document Reset PR
• RegEx Sentence Splitter (or ANNIE Sentence Splitter)
• ANNIE English Tokeniser
• ANNIE POS Tagger
• GATE Morphological Analyser
• Flexible Gazetteer
8. Create a document to process with the new application; for example, if the ontology
was http://gate.ac.uk/ns/gate-kb, then the document could be the GATE home
page: http://gate.ac.uk. Run application and then investigate the results further.
All annotations are of type Lookup, with additional features that give details about
the resources they are referring to in the given ontology.
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13.10
Large KB Gazetteer
The large KB gazetteer provides support for ontology-aware NLP. You can load any ontology
from RDF and then use the gazetteer to obtain lookup annotations that have both instance
and class URI.
The large KB gazetteer is available as the plugin Gazetteer LKB.
The current version of the large KB gazetteer does not use GATE ontology language resources. Instead, it uses its own mechanism to load and process ontologies. The current
version is likely to change significantly in the near future.
The Large KB gazetteer grew from a component in the semantic search platform Ontotext KIM. The gazetteer is developed by people from the KIM team (see http://nmwiki.
ontotext.com/lkb_gazetteer/team-list.html). You may find the name kim left in several places in the source code, documentation or source files.
13.10.1
Quick usage overview
• To use the Large KB gazetteer, set up your dictionary first.
The dictionary is a folder with some configuration files.
Use the samples at
GATE HOME/plugins/Gazetteer LKB/samples as a guide or download a prebuilt dictionary from http://ontotext.com/kim/lkb gazetteer/dictionaries.
• Load GATE HOME/plugins/Gazetteer LKB as a CREOLE plugin. See Section 3.5 for
details.
• Create a new ‘Large KB Gazetteer’ processing resource (PR). Put the folder of the
dictionary you created in the ‘dictionaryPath’ parameter. You can leave the rest of
the parameters as defaults.
• Add the PR to your GATE application. The gazetteer doesn’t require a tokenizer or
the output of any other processing resources.
• The gazetteer will create annotations with type ‘Lookup’ and two features; ‘inst’, which
contains the URI of the ontology instance, and ‘class’ which contains the URI of the
ontology class that instance belongs to.
13.10.2
Dictionary setup
The dictionary is a folder with some configuration files.
GATE HOME/plugins/Gazetteer LKB/samples.
You can find samples at
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Setting up your own dictionary is easy. You need to define your RDF ontology and then
specify a SPARQL or SERQL query that will retrieve a subset of that ontology as a dictionary.
config.ttl is a Turtle RDF file which configures a local RDF ontology or connection to a
remote Sesame RDF database.
If you want to see examples of how to use local RDF files, please check samples/dictionary from local ontology/config.ttl. The Sesame repository configuration section configures a local Ontotext SwiftOWLIM database that loads a list of RDF files. Simply create a list of your RDF files and reuse the rest of the configuration. The sample configuration support datasets with 10,000,000 triples with acceptable performance. For
working with larger datasets, advanced users can substitute SwiftOWLIM with another
Sesame RDF engine. In that case, make sure you add the necessary JARs to the list
in GATE HOME/plugins/Gazetteer LKB/creole.xml. For example, Ontotext BigOWL is
a Sesame RDF engine that can load billions of triples on desktop hardware.
Since any Sesame repository can be configured in config.ttl, the Large KB Gazetteer can extract dictionaries from all significant RDF databases. See the page on database compatibility
for more information.
query.txt contains a SPARQL query. You can write any query you like, as long as its
projection contains at least two columns in the following order: label and instance. As an
option, you can also add a third column for the ontology class of the RDF entity. Below you
can see a sample query, which creates a dictionary from the names and the unique identifiers
of 10,000 entertainers in DbPedia.
PREFIX opencyc: <http://sw.opencyc.org/2008/06/10/concept/en/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?Name ?Person WHERE {
?Person a opencyc:Entertainer ; rdfs:label ?Name .
FILTER (lang(?Name) = "en")
} LIMIT 10000
Try this query at the Linked Data Semantic Repository.
When you load the dictionary configuration in GATE for the first time, it creates a binary
snapshot of the dictionary. Thereafter it will load only this binary snapshot. If the dictionary configuration is changed, the snapshot will be reinitialized automatically. For more
information, please see the dictionary lifecycle specification.
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13.10.3
Additional dictionary configuration
The config.ttl may contain additional dictionary configuration. Such configuration concerns
only the initial loading of the dictionary from the RDF database. The options are still
being determined and more will appear in future versions. They must be placed below the
repository configuration section as attributes of a dictionary configuration. Here is a sample
config.ttl file with additional configuration.
# Sesame configuration template for a (proxy for a) remote repository
#
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>.
@prefix rep: <http://www.openrdf.org/config/repository#>.
@prefix hr: <http://www.openrdf.org/config/repository/http#>.
@prefix lkbg: <http://www.ontotext.com/lkb_gazetteer#>.
[] a rep:Repository ;
rep:repositoryImpl [
rep:repositoryType "openrdf:HTTPRepository" ;
hr:repositoryURL <http://ldsr.ontotext.com/openrdf-sesame/repositories/owlim>
];
rep:repositoryID "owlim" ;
rdfs:label "LDSR" .
[] a lkbg:DictionaryConfiguration ;
lkbg:caseSensitivity "CASE_INSENSITIVE" .
13.10.4
Processing Resource Configuration
The following options can be set when the gazetteer PR is initialized:
• dictionaryPath; the dictionary folder described above.
• forceCaseSensitive; whether the gazetteer should return case-sensitive matches regardless of the loaded dictionary.
13.10.5
Runtime configuration
• annotationSetName - The annotation set, which will receive the generated lookup
annotations.
• annotationLimit - The maximum number of the generated annotations. NULL or 0
for no limit. Setting limit of the number of the created annotations will reduce the
memory consumption of GATE on large documents. Note that GATE documents
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299
consume gigabytes of memory if there are tens of thousands of annotations in the
document. All PRs that create large number of annotations like the gazetteers and
tokenizers may cause an Out Of Memory error on large texts. Setting that options
limits the amount of memory that the gazetteer will use.
13.10.6
Semantic Enrichment PR
The Semantic Enrichment PR allows adding new data to semantic annotations by querying
external RDF (Linked Data) repositories. It is a companion to the large KB gazetteer that
showcases the usefulness of using Linked Data URI as identifiers.
Here a semantic annotation is an annotation that is linked to an RDF entity by having the
URI of the entity in the ‘inst’ feature of the annotation. For all such annotation of a given
type, this PR runs a SPARQL query against the defined repository and puts a commaseparated list of the values mentioned in the query output in the ‘connections’ feature of the
same annotation.
There is a sample pipeline that features the Semantic Enrichment PR.
Parameters
• inputASName; the annotation set, which annotation will be processed.
• server; the URL of the Sesame 2 HTTP repository. Support for generic SPARQL
endpoints can be implemented if required.
• repositoryId; the ID of the Sesame repository.
• annotationTypes; a list of types of annotation that will be processed.
• query; a SPARQL query pattern.
The query will be processed like this String.format(query, uriFromAnnotation), so you can use parameters like %s or %1$s.
• deleteOnNoRelations; whether we want to delete the annotation that weren’t enriched.
Helps to clean up the input annotations.
13.11
The Shared Gazetteer for multithreaded processing
The DefaultGazetteer (and its subclasses such as the OntoRootGazetteer) compiles its
gazetteer data into a finite state matcher at initialization time. For large gazetteers this
FSM requires a considerable amount of memory. However, once the FSM has been built
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Gazetteers
then (as long as you do not modify it dynamically using Gaze) it is accessed in a readonly manner at runtime. For a multi-threaded application that requires several identical
copies of its processing resources (see section 7.13), GATE provides a mechanism whereby a
single compiled FSM can be shared between several gazetteer PRs that can then be executed
concurrently in different threads, saving the memory that would otherwise be required to
load the lists several times.
This feature is not available in the GATE Developer GUI, as it is only intended for
use in embedded code. To make use of it, first create a single instance of the regular
DefaultGazetteer or OntoRootGazetteer:
FeatureMap params = Factory.newFeatureMap();
params.put("listsUrl", listsDefLocation);
LanguageAnalyser mainGazetteer = (LanguageAnalyser)Factory.createResource(
"gate.creole.gazetteer.DefaultGazetteer", params);
Then create any number of SharedDefaultGazetteer instances, passing this regular
gazetteer as a parameter:
FeatureMap params = Factory.newFeatureMap();
params.put("bootstrapGazetteer", mainGazetteer);
LanguageAnalyser sharedGazetteer = (LanguageAnalyser)Factory.createResource(
"gate.creole.gazetteer.SharedDefaultGazetteer", params);
The SharedDefaultGazetteer instance will re-use the FSM that was built by the
mainGazetteer instead of loading its own.
Chapter 14
Working with Ontologies
GATE provides an API for modeling and manipulating ontologies and comes with two plugins
that provide implementations for the API and several tools for editing ontologies and using
ontologies for document annotation.
Ontologies in GATE are classified as language resources. In order to create an ontology
language resource, the user must first load one of the two plugins containing an ontology
implementation.
The following implementations and ontology related tools are provided as plugins:
• Plugin Ontology OWLIM2 provides an implementation that is fully backwardscompatible with the implementation that was part of GATE prior to version 5.1 (see
Section 14.4).
• Plugin Ontology provides a modified and current implementation (see Section 14.3).
Unless noted otherwise, all information in this chapter applies to this implementation.
• Plugin Ontology Tools provides a simple graphical ontology editor (see Section 14.5)
and OCAT, a tool for interactive ontology based document annotation (see Section 14.6). It also provides a gazetteer processing resource, OntoGaz, that allows
the mapping of linear gazetteers to classes in an ontology (see Section 13.4).
• Plugin Gazetteer Ontology Based provides the ‘Onto Root Gazetteer’ for the automatic creating of a gazetteer from an ontology (see Section 13.9)
• Plugin Ontology BDM Computation can be used to compute BDM scores (see Section 10.6).
• Plugin Gazetteer LKB provides a processing resource for creating annotations based
on the contents of a large ontology.
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GATE ontology support aims to simplify the use of ontologies both within the set of GATE
tools and for programmers using the GATE ontology API. The GATE ontology API hides
the details of the actual backend implementation and allows a simplified manipulation of
ontologies by modeling ontology resources as easy-to-use Java objects. Ontologies can be
loaded from and saved to various serialization formats.
The GATE ontology support roughly conforms to the representation, manipulation and
inference that conforms to what is supported in OWL-Lite (see http://www.w3.org/TR/
owl-features/). This means that a user can represent information in an ontology that
conforms to OWL-Lite and that the GATE ontology model will provide inferred information
equivalent to what an OWL-Lite reasoner would provide. The GATE ontology model makes
an attempt to also to some extend provide useful information for ontologies that do not
conform to OWL-Lite: RDFS, OWL-DL, OWL-Full or OWL2 ontologies can be loaded but
GATE might ignore part of all contents of those ontologies, or might only provide part of,
or incorrect inferred facts for such ontologies.
The GATE API tries to prevent clients from modifying an ontology that conforms to OWLLite to become OWL-DL or OWL-Full and also tries to prevent or warn about some of
the most common errors that would make the ontology inconsistent. However, the current
implementation is not able to prevent all such errors and has no way of finding out if an
ontology conforms to OWL-Lite or is inconsistent.
14.1
Data Model for Ontologies
14.1.1
Hierarchies of Classes and Restrictions
Class hierarchy (or taxonomy) plays the central role in the ontology data model. This
consists of a set of ontology classes (represented by OClass objects in the ontology API)
linked by subClassOf, superClassOf and equivalentClassAs relations. Each ontology
class is identified by an URI (unless it is a restriction or an anonymous class, see below).
The URI of each ontology resource must be unique.
Each class can have a set of superclasses and a set of subclasses; these are used to build the
class hierarchy. The subClassOf and superClassOf relations are transitive and methods
are provided by the API for calculating the transitive closure for each of these relations given
a class. The transitive closure for the set of superclasses for a given class is a set containing
all the superclasses of that class, as well as all the superclasses of its direct superclasses, and
so on until no more are found. This calculation is finite, the upper bound being the set of all
the classes in the ontology. A class that has no superclasses is called a top class. An ontology
can have several top classes. Although the GATE ontology API can deal with cycles in the
hierarchy graph, these can cause problems for processes using the API and probably indicate
an error in the definition of the ontology. Also other components of GATE, like the ontology
editor cannot deal with cyclic class structures and will terminate with an error. Care should
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303
be taken to avoid such situations.
A pair of ontology classes can also have an equivalentClassAs relation, which indicates
that the two classes are virtually the same and all their properties and instances should be
shared.
A restriction (represented by Restriction objects in the GATE ontology API) is an anonymous class (i.e., the class is not identified by an URI/IRI) and is set on an object or a
datatype property to restrict some instances of the specified domain of the property to have
only certain values (also known as value constraint) or certain number of values (also known
as cardinality restriction) for the property. Thus for each restriction there exists at least
three triples in the repository. One that defines resource as a restriction, another one that
indicates on which property the restriction is specified, and finally the third one that indicates what is the constraint set on the cardinality or value on the property. There are six
types of restrictions:
1. Cardinality Restriction (owl:cardinalityRestriction): the only valid values for this
restriction in OWL-Lite are 0 and 1. A cardinality restriction set to either 0 or 1 implies
both a MinCardinality Restriction and a MaxCardinality Restriction set to the same
value.
2. MinCardinality Restriction (owl:minCardinalityRestriction)
3. MaxCardinality Restriction (owl:maxCardinalityRestriction)
4. HasValue Restriction (owl:hasValueRestriction)
5. AllValuesFrom Restriction (owl:allValuesFromRestriction)
6. SomeValuesFrom Restriction (owl:someValuesFromRestriction)
Please visit the OWL Reference for more detailed information on restrictions.
14.1.2
Instances
Instances, also often called individuals are objects that belong to classes. Like named classes,
each instance is identified by an URI. Each instance can belong to one or more classes and
can have properties with values. Two instances can have the sameInstanceAs relation,
which indicates that the property values assigned to both instances should be shared and
that all the properties applicable to one instance are also valid for the other. In addition,
there is a differentInstanceAs relation, which declares the instances as disjoint.
Instances are represented by OInstance objects in the API. API methods are provided for
getting all the instances in an ontology, all the ones that belong to a given class, and all the
property values for a given instance. There is also a method to retrieve a list of classes that
the instance belongs to, using either transitive or direct closure.
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14.1.3
Hierarchies of Properties
The last part of the data model is made up of hierarchies of properties that can be associated
with objects in the ontology. The specification of the type of objects that properties apply to
is done through the means of domains. Similarly, the types of values that a property can take
are restricted through the definition of a range. A property with a domain that is an empty
set can apply to instances of any type (i.e. there are no restrictions given). Like classes,
properties can also have superPropertyOf, subPropertyOf and equivalentPropertyAs
relations among them.
GATE supports the following property types:
1. Annotation Property:
An annotation property is associated with an ontology resource (i.e. a class,
property or instance) and can have a Literal as value.
A Literal is a
Java object that can refer to the URI of any ontology resource or a string
(http://www.w3.org/2001/XMLSchema#string) with the specified language or a data
type (discussed below) with a compatible value. Two annotation properties can not
be declared as equivalent. It is also not possible to specify a domain or range for an
annotation property or a super or subproperty relation between two annotation properties. Five annotation properties, predefined by OWL, are made available to the user
whenever a new ontology instance is created:
• owl:versionInfo,
• rdfs:label,
• rdfs:comment,
• rdfs:seeAlso, and
• rdfs:isDefinedBy.
In other words, even when the user creates an empty ontology, these annotation properties are created automatically and available to users.
2. Datatype Property:
A datatype property is associated with an ontology instance and can have a Literal
value that is compatible with its data type . A data type can be one of the pre-defined
data types in the GATE ontology API:
http://www.w3.org/2001/XMLSchema#boolean
http://www.w3.org/2001/XMLSchema#byte
http://www.w3.org/2001/XMLSchema#date
http://www.w3.org/2001/XMLSchema#decimal
http://www.w3.org/2001/XMLSchema#double
http://www.w3.org/2001/XMLSchema#duration
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http://www.w3.org/2001/XMLSchema#float
http://www.w3.org/2001/XMLSchema#int
http://www.w3.org/2001/XMLSchema#integer
http://www.w3.org/2001/XMLSchema#long
http://www.w3.org/2001/XMLSchema#negativeInteger
http://www.w3.org/2001/XMLSchema#nonNegativeInteger
http://www.w3.org/2001/XMLSchema#nonPositiveInteger
http://www.w3.org/2001/XMLSchema#positiveInteger
http://www.w3.org/2001/XMLSchema#short
http://www.w3.org/2001/XMLSchema#string
http://www.w3.org/2001/XMLSchema#time
http://www.w3.org/2001/XMLSchema#unsignedByte
http://www.w3.org/2001/XMLSchema#unsignedInt
http://www.w3.org/2001/XMLSchema#unsignedLong
http://www.w3.org/2001/XMLSchema#unsignedShort
A set of ontology classes can be specified as a property’s domain; in that case the
property can be associated with the instance belonging to all of the classes specified in
that domain only (the intersection of the set of domain classes).
Datatype properties can have other datatype properties as subproperties.
3. Object Property:
An object property is associated with an ontology instance and has an instance as
value. A set of ontology classes can be specified as property’s domain and range. Then
the property can only be associated with the instances belonging to all of the classes
specified as the domain. Similarly, only the instances that belong to all the classes
specified in the range can be set as values.
Object properties can have other object properties as subproperties.
4. RDF Property:
RDF properties are more general than datatype or object properties. The GATE
ontology API uses RDFProperty objects to hold datatype properties, object properties,
annotation properties or actual RDF properties (rdf:Property).
Note: The use of RDFProperty objects for creating, or manipulating RDF properties
is carried over from previous implementations for compatibility reasons but should be
avoided.
All properties (except the annotation properties) can be marked as functional properties,
which means that for a given instance in their domain, they can only take at most one value,
i.e. they define a function in the algebraic sense. Properties inverse to functional properties
are marked as inverse functional. If one likes ontology properties with algebraic relations,
the semantics of these become apparent.
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14.1.4
URIs
URIs are used to identify resources (instances, classes, properties) in an ontology. All URIs
that identify classes, instances, or properties in an ontology must consist of two parts:
• a name part: this is the part after the last slash (#) or the first hash (#) in the URI.
This part of the URI is often used as a shorthand name for the entity (e.g. in the
ontology editor) and is often called a fragment identifier
• a namespace part: the part that precedes the name, including the trailing slash or hash
character.
URIs uniquely identify resources: each resource can have at most one URI and each URI
can be associated with at most one resource.
URIs are represented by OURI objects in the API. The Ontology object provides factory
methods to create OURIs from a complete URI string or by appending a name to the default
namespace of the ontology. However it is the responsibility of the caller to ensure that any
strings that are passed to these factory methods do in fact represent valid URIs. GATE
provides some helper methods in the OUtils class to help with encoding and decoding URI
strings.
14.2
Ontology Event Model
An Ontology Event Model (OEM) is implemented and incorporated into the new GATE
ontology API. Under the new OEM, events are fired when a resource is added, modified or
deleted from the ontology.
An interface called OntologyModificationListener is created with five methods (see below) that need to be implemented by the listeners of ontology events.
public void resourcesRemoved(Ontology ontology, String[] resources);
This method is invoked whenever an ontology resource (a class, property or instance) is
removed from the ontology. Deleting one resource can also result into the deletion of the
other dependent resources. For example, deleting a class should also delete all its instances
(more details on how deletion works are explained later). The second parameter, an array
of strings, provides a list of URIs of resources deleted from the ontology.
public void resourceAdded(Ontology ontology, OResource resource);
This method is invoked whenever a new resource is added to the ontology. The parameters
provide references to the ontology and the resource being added to it.
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public void ontologyRelationChanged(Ontology ontology, OResource resource1,
OResource resource2, int eventType);
This method is invoked whenever a relation between two resources (e.g. OClass and OClass,
RDFPRoeprty, RDFProeprty, etc) is changed. Example events are addition or removal of
a subclass or a subproperty, two classes or properties being set as equivalent or different
and two instances being set as same or different. The first parameter is the reference to the
ontology, the next two parameters are the resources being affected and the final parameters
is the event type. Please refer to the list of events specified below for different types of
events.
public void resourcePropertyValueChanged(Ontology ontology,
OResource resource, RDFProperty
property, Object value, int eventType)
This method is invoked whenever any property value is added or removed to a resource.
The first parameter provides a reference to the ontology in which the event took place. The
second provides a reference to the resource affected, the third parameter provides a reference
to the property for which the value is added or removed, the fourth parameter is the actual
value being set on the resource and the fifth parameter identifies the type of event.
public void ontologyReset(Ontology ontology)
This method is called whenever ontology is reset. In other words when all resources of the
ontology are deleted using the ontology.cleanup method.
The OConstants class defines the static constants, listed below, for various event types.
public
public
public
public
public
public
public
public
public
public
public
public
public
public
public
public
public
static
static
static
static
static
static
static
static
static
static
static
static
static
static
static
static
static
final
final
final
final
final
final
final
final
final
final
final
final
final
final
final
final
final
int
int
int
int
int
int
int
int
int
int
int
int
int
int
int
int
int
OCLASS_ADDED_EVENT;
ANONYMOUS_CLASS_ADDED_EVENT;
CARDINALITY_RESTRICTION_ADDED_EVENT;
MIN_CARDINALITY_RESTRICTION_ADDED_EVENT;
MAX_CARDINALITY_RESTRICTION_ADDED_EVENT;
HAS_VALUE_RESTRICTION_ADDED_EVENT;
SOME_VALUES_FROM_RESTRICTION_ADDED_EVENT;
ALL_VALUES_FROM_RESTRICTION_ADDED_EVENT;
SUB_CLASS_ADDED_EVENT;
SUB_CLASS_REMOVED_EVENT;
EQUIVALENT_CLASS_EVENT;
ANNOTATION_PROPERTY_ADDED_EVENT;
DATATYPE_PROPERTY_ADDED_EVENT;
OBJECT_PROPERTY_ADDED_EVENT;
TRANSTIVE_PROPERTY_ADDED_EVENT;
SYMMETRIC_PROPERTY_ADDED_EVENT;
ANNOTATION_PROPERTY_VALUE_ADDED_EVENT;
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public
public
public
public
public
public
public
public
public
public
public
public
public
public
public
static
static
static
static
static
static
static
static
static
static
static
static
static
static
static
final
final
final
final
final
final
final
final
final
final
final
final
final
final
final
int
int
int
int
int
int
int
int
int
int
int
int
int
int
int
DATATYPE_PROPERTY_VALUE_ADDED_EVENT;
OBJECT_PROPERTY_VALUE_ADDED_EVENT;
RDF_PROPERTY_VALUE_ADDED_EVENT;
ANNOTATION_PROPERTY_VALUE_REMOVED_EVENT;
DATATYPE_PROPERTY_VALUE_REMOVED_EVENT;
OBJECT_PROPERTY_VALUE_REMOVED_EVENT;
RDF_PROPERTY_VALUE_REMOVED_EVENT;
EQUIVALENT_PROPERTY_EVENT;
OINSTANCE_ADDED_EVENT;
DIFFERENT_INSTANCE_EVENT;
SAME_INSTANCE_EVENT;
RESOURCE_REMOVED_EVENT;
RESTRICTION_ON_PROPERTY_VALUE_CHANGED;
SUB_PROPERTY_ADDED_EVENT;
SUB_PROPERTY_REMOVED_EVENT;
An ontology is responsible for firing various ontology events. Object wishing to listen to
the ontology events must implement the methods above and must be registered with the
ontology using the following method.
addOntologyModificationListener(OntologyModificationListener oml);
The following method cancels the registration.
removeOntologyModificationListener(OntologyModificationListener oml);
14.2.1
What Happens when a Resource is Deleted?
Resources in an ontology are connected with each other. For example, one class can be a
sub or superclass of another classes. A resource can have multiple properties attached to it.
Taking these various relations into account, change in one resource can affect other resources
in the ontology. Below we describe what happens (in terms of what does the GATE ontology
API do) when a resource is deleted.
• When a class is deleted
– A list of all its super classes is obtained. For each class in this list, a list of its
subclasses is obtained and the deleted class is removed from it.
– All subclasses of the deleted class are removed from the ontology. A list of all its
equivalent classes is obtained. For each class in this list, a list of its equivalent
classes is obtained and the deleted class is removed from it.
– All instances of the deleted class are removed from the ontology.
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– All properties are checked to see if they contain the deleted class as a member
of their domain or range. If so, the respective property is also deleted from the
ontology.
• When an instance is deleted
– A list of all its same instances is obtained. For each instance in this list, a list of
its same instances is obtained and the deleted instance is removed.
– A list of all instances set as different from the deleted instance is obtained. For
each instance in this list, a list of instances set as different from it is obtained and
the deleted instance is removed.
– All the instances of ontology are checked to see if any of their set properties have
the deleted instance as value. If so, the respective set property is altered to remove
the deleted instance.
• When a property is deleted
– A list of all its super properties is obtained. For each property in this list, a list
of its sub properties is obtained and the deleted property is removed.
– All sub properties of the deleted property are removed from the ontology.
– A list of all its equivalent properties is obtained. For each property in this list, a
list of its equivalent properties is obtained and the deleted property is removed.
– All instances and resources of the ontology are checked to see if they have the
deleted property set on them. If so the respective property is deleted.
14.3
The Ontology Plugin: Current Implementation
The plugin Ontology contains the current ontology API implementation. This implementation provides the additions and enhancements introduced into the GATE ontology API as
of release 5.1. It is based on a backend that uses Sesame version 2 and OWLIM version 3.
Before any ontology-based functionality can be used, the plugin must be loaded into GATE.
To do this in the GATE Developer GUI, select the ‘Manage CREOLE Plugins’ option from
the ‘File’ menu and check the ‘Load now’ checkbox for the ‘Ontology’ plugin, then click
OK. After this, the context menu for Language Resources will include the following ontology
language resources:
• OWLIMOntology: this is the standard language resource to use in most situations.
It allows the user to create a new ontology backed by files in a local directory and
optionally load ontology data into it.
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• OWLIMOntology DEPRECATED: this language resource has the same functionality as
OWLIMOntology but uses the exactly same package and class name as the language
resource in the plugin Ontology_OWLIM2. This LR is provided to allow an easier
upgrade of existing pipelines to the new implementation but users should move the the
OWLIMOntology LR as soon as possible.
• ConnectSesameOntology: This language resources allows the use of ontologies that are
already stored in a Sesame2 repository which is either stored in a directory or accessible
from a server. This is useful for quickly re-using a very large ontology that has been
previously created as a persistent OWLIMOntology language resource.
• CreateSesameOntology: This language resource allows the user to create a new empty
ontology by specifying the repository configuration for creating the sesame repository.
Note:This is for advanced uses only!
Each of these language resources is explained in more detail in the following sections.
To make the plugin available to your GATE Embedded application, load the plugin prior to
creating one of the ontology language resources using the following code:
1
2
3
4
5
// Find the directory for the Ontology plugin
File pluginHome =
new File ( new File ( Gate . getGateHome () , " plugins " ) , " Ontology " );
// Load the plugin from that directory
Gate . ge tCreol eRegis ter (). r eg i s te r D ir e c to r i es ( pluginHome . toURI (). toURL ());
14.3.1
The OWLIMOntology Language Resource
The OWLIMOntology language resource is the main ontology language resource provided by
the plugin and provides a similar functionality to the OWLIMOntologyLR language resource
provided by the pre-5.1 implementation and provided by the Ontology_OWLIM2 plugin from
version 5.1 on. This language resource creates an in-memory store backed by files in a
directory on the file system to hold the ontology data.
To create a new OWLIM Ontology resource, select ‘OWLIM Ontology’ from the right-click
‘New’ menu for language resources. A dialog as shown in Figure 14.1 appears with the
following parameters to fill in or change:
• Name (optional): if no name is given, a default name will be generated, if an ontology
is loaded from an URL, based on that URL, otherwise based on the language resource
name.
• baseURI (optional): the URI to be used for resolving relative URI references in the
ontology during loading.
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• dataDirectoryName (optional): the name of an existing directory on the file system
where the directory will be created that backs the ontology store. The name of the
directory that will be created within the data directory will be GATE_OWLIMOntology_
followed by a string representation of the system time. If this parameter is not specified,
the value for system property java.io.tmpdir is used, if this is not set either an error
is raised.
• loadImports (optional): either true or false. If set to false all ontology import specifications found in the loaded ontology are ignored. This parameter is ignored if no
ontology is loaded when the language resource is created.
• mappingsURL (optional): the URL of a text file containing import mappings specifications. See section 14.3.5 for a description of the mappings file. If no URL is specified,
the GATE will interpret each import URI found as an URL and try to import the data
from that URL. If the URI is not absolute it will get resolved against the base URI.
• persistent (optional): true or false: if false, the directory created inside the data directory is removed when the language resource is closed, otherwise, that directory is kept.
The ConnectSesameOntology language resource can be used at a later time to connect
to such a directory and create an ontology language resource for it (see Section 14.3.2).
• rdfXmlUrl (optional): an URL specifying the location of an ontology in RDF/XML serialization format (see http://www.w3.org/TR/rdf-syntax-grammar/) from which to
load initial ontology data from. The parameter name can be changed from rdfXmlUrl
to n3Url to indicate N3 serialization format (see http://www.w3.org/DesignIssues/
Notation3.html), to ntriplesUrl to indicate N-Triples format (see http://www.
w3.org/TR/2004/REC-rdf-testcases-20040210/#ntriples), and to turtleUrl to
indicate TURTLE serialization format (see http://www.w3.org/TeamSubmission/
turtle/). If this is left blank, no ontology is loaded and an empty ontology language
resource is created.
Figure 14.1: The New OWLIM Ontology Dialog
Note: you could create a language resource such as OWLIM Ontology from GATE Developer
successfully, but you will not be able to browse/edit the ontology unless you loaded Ontology
Tools plugin beforehand.
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Additional ontology data can be loaded into an existing ontology language resource by selecting the ‘Load’ option from the language resource’s context menu. This will show the
dialog shown in figure 14.2. The parameters in this dialog correspond to the parameters
in the dialog for creating a new ontology with the addition of one new parameter: ‘load as
import’. If this parameter is checked, the ontology data is loaded specifically as an ontology
import. Ontology imports can be excluded from what is saved at a later time.
Figure 14.2: The Load Ontology Dialog
Figure 14.3 shows the ontology save dialog that is shown when the option ‘Save as. . . ’ is
selected from the language resource’s context menu. The parameter ‘include imports’ allows
the user to specify if the data that has been loaded through imports should be included in
the saved data or not.
Figure 14.3: The Save Ontology Dialog
14.3.2
The ConnectSesameOntology Language Resource
This ontology language resource can be created from either a directory on the local file
system that holds an ontology backing store (as created in the ‘data directory’ for the
‘OWLIM Ontology’ language resource), or from a sesame repository on a server that holds
an OWLIM ontology store.
This is very useful when using very large ontologies with GATE. Loading a very large ontology from a serialized format takes a significant amount of time because the file has to be
deserialized and all implied facts have to get generated. Once an ontology has been loaded
into a persisting OWLIMOntology language resource, the ConnectSesameOntology language
resource can be used with the directory created to re-connect to the already de-serialized
and inferred data much faster.
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Figure 14.4 shows the dialog for creating a ConnectSesameOntology language resource.
• repositoryID: the name of the sesame repository holding the ontology store. For a
backing store created with the ‘OWLIM Ontology’ language resource, this is always
‘owlim3’.
• repositoryLocation: the URL of the location where to find the repository holding the
ontology store. The URL can either specify a local directory or an HTTP server.
For a backing store created with the ‘OWLIM Ontology’ language resource this is
the directory that was created inside the data directory (the name of the directory
starting with GATE_OWLIMOntology_). If the URL specifies a HTTP server which
requires authentification, the user-ID and password have to be included in the URL
(e.g. http://userid:passwd@localhost:8080/openrdf-sesame).
Note that this ontology language resource is only supported when connected with an
OWLIM3 repository with the owl-max ruleset and partialRDFS optimizations disabled!
Connecting to any other repository is experimental and for expert users only! Also note that
connecting to a repository that is already in use by GATE or any other application is not
supported and might result in unwanted or erroneous behavior!
Figure 14.4: The New ConnectSesameOntology Dialog
14.3.3
The CreateSesameOntology Language Resource
This ontology language resource can be directly created from a Sesame2 repository configuration file. This is an experimental language resource intended for expert users only. This
can be used to create any kind of Sesame2 repository, but the only repository configuration supported by GATE and the GATE ontology API is an OWLIM repository with the
owl-max ruleset and partialRDFS optimizations disabled. The dialog for creating this
language resource is shown in Figure 14.5.
14.3.4
The OWLIM2 Backwards-Compatible Language Resource
This language resource is shown as “OWLIM Ontology DEPRECATED” in the ‘New
Language Resource’ submenu from the ‘File’ menu. It provides the “OWLIM Ontology” language resource in a way that attempts maximum backwards-compatibility with
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Figure 14.5: The New CreateSesameOntology Dialog
the ontology language resource provided by prior versions or the Ontology OWLIM2 language resource. This means, the class name is identical to those language resources
gate.creole.ontology.owlim.OWLIMOntologyLR) and the parameters are made compatible. This means that the parameter defaultNameSpace is added as an alias for the parameter
baseURI (also the methods setPersistsLocation and getPersistLocation are available
for legacy Java code that expects them, but the persist location set that way is not actually
used).
In addition, this language resource will still automatically add the resource name of a resource
as the String value for the annotation property “label”.
14.3.5
Using Ontology Import Mappings
If an ontology is loaded that contains the URIs of imported ontologies using owl:imports,
the plugin will try to automatically resolve those URIs to URLs and load the ontology file
to be imported from the location corresponding to the URL. This is done transitively, i.e.
import specifications contained in freshly imported ontologies are resolved too.
In some cases one might want to suppress the import of certain ontologies or one might want
to load the data from a different locatin, e.g. from a file on the local file system instead.
With the OWLIMOntology language resource this can be achieved by specifying an import
mappings file when creating the ontology.
An import mappings file (see figure 14.6 for an example) is a plain file that maps specific
import URIs to URLs or to nothing at all. Each line that is not empty or does not start
with a hash (#) indicating a comment line must contain a URI. If the URI is not followed
by anything, this URI will be ignored when processing imports. If the URI is followed by
something, this is interpreted as a URL that is used for resolving the import of the URI. Local
files can be specified as file: URLs or by just giving the absolute or relative pathname
of the file in Linux path notation (forward slashes as path separators). At the moment,
filenames with embedded whitespace are not supported. If a pathname is relative it will be
resolved relative to the directory which contains the mappings file.
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# map this import to another web url
http://proton.semanticweb.org/2005/04/protont http://mycompany.com/owl/protont.owl
# map this import to a file in the same directory as the mappings file
http://proton.semanticweb.org/2005/04/protons protons.owl
# ignore this import
http://somewhere.com/reallyhugeimport
Figure 14.6: An example import mappings file
14.3.6
Using BigOWLIM
The GATE ontology plugin is based on SwiftOWLIM for storing the ontology and managing
inference. SwiftOWLIM is an in-memory store and the maximum size of ontologies that can
be stored is limited by the available memory.
BigOWLIM (see http://www.ontotext.com/owlim/big/) can handle huge ontologies and
is not limited by available memory. BigOWLIM is a commercial product and needs to be separately obtained and installed for use with the GATE ontology plugin. See the BigOWLIM
installation guide on how to set up BigOWLIM on a Tomcat server and how to create
BigOWLIM on the server with the Sesame console program.
The ontology plugin can easily and without any additional installation be used with
BigOWLIM repositories by using the ConnectSesameOntology LR (see section 14.3.2) to
connect to a BigOWLIM repository on a remote Tomcat server.
14.4
The Ontology OWLIM2 plugin:
compatible implementation
backwards-
14.4.1
The OWLIMOntologyLR Language Resource
This implementation is identical to the implementation that was part of GATE core before
version 5.1. It is based on SwiftOWLIM version 2 and Sesame version 1.
In order to load an ontology in an OWLIM repository, the user has to provide certain
configuration parameters. These include the name of the repository, the URL of the ontology,
the default name space, the format of the ontology (RDF/XML, N3, NTriples and Turtle),
the URLs or absolute locations of the other ontologies to be imported, their respective name
spaces and so on. Ontology files, based on their format, are parsed and persisted in the
NTriples format.
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In order to utilize the power of OWLIM and the simplicity of GATE ontology API, GATE
provides an implementation of the OWLIM Ontology. Its basic purpose is to hide all the
complexities of OWLIM and Sesame and provide an easy to use API and interface to create,
load, save and update ontologies. Based on certain parameters that the user provides when
instantiating the ontology, a configuration file is dynamically generated to create a dummy
repository in memory (unless persistence is specified).
When creating a new ontology, one can use an existing file to pre-populate it with data.
If no such file is provided, an empty ontology is created. A detailed description for all the
parameters that are available for new ontologies follows:
1. defaultNameSpace is the base URI to be used for all new items that are only mentioned
using their local name. This can safely be left empty, in which case, while adding new
resources to the ontology, users are asked to provide name spaces for each new resource.
2. As indicated earlier, OWLIM supports four different formats: RDF/XML, NTriples,
Turtle and N3. According to the format of the ontology file, user should select one of
the four URL options (rdfXmlURL, ntriplesURL, turtleURL and n3URL (not supported
yet)) and provide a URL pointing to the ontology data.
Once an ontology is created, additional data can be loaded that will be merged with the
existing information. This can be done by right-clicking on the ontology in the resources tree
in GATE Developer and selecting ‘Load ... data’ where ‘...’ is one of the supported formats.
Other options available are cleaning the ontology (deleting all the information from it) and
saving it to a file in one of the supported formats.
Ontology can be saved in different formats (rdf/xml, ntriples, n3 and turtle) using the
options provided in the context menu that can be invoked by right clicking on the instance
of an ontology in GATE Developer. All the changes made to the ontology are logged and
stored as an ontology feature. Users can also export these changes to a file by selecting the
‘Save Ontology Event Log’ option from the context menu. Similarly, users can also load
the exported event log and apply the changes on a different ontology by using the ‘Load
Ontology Event Log’ option. Any change made to the ontology can be described by a set
of triples either added or deleted from the repository. For example, in GATE Embedded,
addition of a new instance results into addition of two statements into the repository:
// Adding a new instance "Rec1" of type "Recognized"
// Here + indicates the addition
+ <http://proton.semanticweb.org/2005/04/protons#Rec1>
<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>
<http://proton.semanticweb.org/2005/04/protons#Recognized>
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// Adding a label (annotation property) to the instance with
// value "Rec Instance"
+ <http://proton.semanticweb.org/2005/04/protons#Rec1>
<http://www.w3.org/2000/01/rdf-schema#label>
<Rec Instance>
<http://www.w3.org/2001/XMLSchema#string>
The event log therefore contains a list of such triples, the latest change being at the bottom
of the change log. Each triple consists of a subject followed by a predicate followed by an
object. Below we give an illustration explaining the syntax used for recording the changes.
// Adding a new instance "Rec1" of type "Recognized"
// Here + indicates the addition
+ <http://proton.semanticweb.org/2005/04/protons#Rec1>
<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>
<http://proton.semanticweb.org/2005/04/protons#Recognized>
// Adding a label (annotation property) to the instance with
// value "Rec Instance"
+ <http://proton.semanticweb.org/2005/04/protons#Rec1>
<http://www.w3.org/2000/01/rdf-schema#label>
<Rec Instance>
<http://www.w3.org/2001/XMLSchema#string>
// Adding a new class called TrustSubClass
+ <http://proton.semanticweb.org/2005/04/protons#TrustSubClass>
<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>
<http://www.w3.org/2002/07/owl#Class>
// TrustSubClass is a subClassOf the class Trusted
+ <http://proton.semanticweb.org/2005/04/protons#TrustSubClass>
<http://www.w3.org/2000/01/rdf-schema#subClassOf>
<http://proton.semanticweb.org/2005/04/protons#Trusted>
// Deleting a property called hasAlias and all relevant statements
// Here - indicates the deletion
// * indicates any value in place
- <http://proton.semanticweb.org/2005/04/protons#hasAlias> <*> <*>
- <*> <http://proton.semanticweb.org/2005/04/protons#hasAlias> <*>
- <*> <*> <http://proton.semanticweb.org/2005/04/protons#hasAlias>
// Deleting a label set on the instance Rec1
- <http://proton.semanticweb.org/2005/04/protons#Rec1>
<http://www.w3.org/2000/01/rdf-schema#label>
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<Rec Instance>
<http://www.w3.org/2001/XMLSchema#string>
// Reseting the entire ontology (Deleting all statements)
- <*> <*> <*>
14.5
GATE Ontology Editor
GATE’s ontology support also includes a viewer/editor that can be used within GATE
Developer to navigate an ontology and quickly inspect the information relating to any of the
objects defined in it—classes and restrictions, instances and their properties. Also, resources
can be deleted and new resources can be added through the viewer.
Before the ontology editor can be used, one of the ontology implementation plugins must be
loaded. In addition the Ontology_Tools must be loaded.
Note: To make it possible to show a loaded ontology in the ontology editor, the
Ontology_Tools plugin must be loaded before the ontology language resource is created.
Figure 14.7: The GATE Ontology Viewer
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The viewer is divided into two areas. One on the left shows separate tabs for hierarchy of
classes and instances and for (as of Gate 4) hierarchy of properties. The view on right hand
side shows the details pertaining of the object currently selected in the other two.
First tab on the left view displays a tree which shows all the classes and restrictions defined in
the ontology. The tree can have several root nodes—one for each top class in the ontology.
The same tree also shows each instances for each class. Note: Instances that belong to
several classes are shown as children of all the classes they belong to.
Second tab on the left view displays a tree of all the properties defined in the ontology. This
tree can also have several root nodes—one for each top property in the ontology. Different
types of properties are distinguished by using different icons.
Whenever an item is selected in the tree view, the right-hand view is populated with the
details that are appropriate for the selected object. For an ontology class, the details include
the brief information about the resource such as the URI of the selected class, type of the
selected class etc., set of direct superclasses, the set of all superclasses using the transitive
closure, the set of direct subclasses, the set of all the subclasses, the set of equivalent classes,
the set of applicable property types, the set of property values set on the selected class, and
the set of instances that belong to the selected class. For a restriction, in addition to the
above information, it displays on which property the restriction is applicable to and what
type of the restriction that is.
For an instance, the details displayed include the brief information about the instance, set
of direct types (the list of classes this instance is known to belong to), the set of all types
this instance belongs to (through the transitive closure of the set of direct types), the set of
same instances, the set of different instances and the values for all the properties that are
set.
When a property is selected, different information is displayed in the right-hand view according to the property type. It includes the brief information about the property itself,
set of direct superproperties, the set of all superproperties (obtained through the transitive
closure), the set of direct subproperties, the set of all subproperties (obtained through the
transitive closure), the set of equivalent properties, and domain and range information.
As mentioned in the description of the data model, properties are not directly linked to the
classes, but rather define their domain of applicability through a set of domain restrictions.
This means that the list of properties should not really be listed as a detail for class objects
but only for instances. It is however quite useful to have an indication of the types of
properties that could apply to instances of a given class. Because of the semantics of property
domains, it is not possible to calculate precisely the list of applicable properties for a given
class, but only an estimate of it. If a property for instance requires its domain instances to
belong to two different classes then it cannot be known with certitude whether it is applicable
to either of the two classes—it does not apply to all instances of any of those classes, but
only to those instances the two classes have in common. Because of this, such properties
will not be listed as applicable to any class.
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The information listed in the details pane is organised in sub-lists according to the type of
the items. Each sub-list can be collapsed or expanded by clicking on the little triangular
button next to the title. The ontology viewer is dynamic and will update the information
displayed whenever the underlying ontology is changed through the API.
When you double click on any resource in the details table, the respective resource is selected
in the class or in the property tree and the selected resource’s details are shown in the details
table. To change a property value, user can double click on a value of the property (second
column) and the relevant window is shown where user is asked to provide a new value. Along
with each property value, a button (with red X caption) is provided. If user wants to remove
a property value he or she can click on the button and the property value is deleted.
A new toolbar has been added at the top of the ontology viewer, which contains the following
buttons to add and delete ontology resources:
• Add new top class (TC)
• Add new subclass (SC)
• Add new instance (I)
• Add new restriction (R)
• Add new Annotation property (A)
• Add new Datatype property (D)
• Add new Object property (O)
• Add new Symmetric property (S)
• Add new Transitive property (T)
• Remove the selected resource(s) (X)
• Search
• Refresh ontology
The tree components allow the user to select more than one node, but the details table on
the right-hand side of the GATE Developer GUI only shows the details of the first selected
node. The buttons in the toolbar are enabled and disabled based on users’ selection of nodes
in the tree.
1. Creating a new top class:
A window appears which asks the user to provide details for its namespace (default
name space if specified), and class name. If there is already a class with same name in
ontology, GATE Developer shows an appropriate message.
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2. Creating a new subclass:
A class can have multiple super classes. Therefore, selecting multiple classes in the
ontology tree and then clicking on the ‘SC’ button, automatically considers the selected
classes as the super classes. The user is then asked for details for its namespace and
class name.
3. Creating a new instance:
An instance can belong to more than one class. Therefore, selecting multiple classes
in the ontology tree and then clicking on the ‘I’ button, automatically considers the
selected classes as the type of new instance. The user is then prompted to provide
details such as namespace and instance name.
4. Creating a new restriction:
As described above, restriction is a type of an anonymous class and is specified on a
property with a constraint set on either the number of values it can take or the type
of value allowed for instances to have for that property. User can click on the blue ‘R’
square button which shows a window for creating a new restriction. User can select
a type of restriction, property and a value constraint for the same. Please note that
restrictions are considered as anonymous classes and therefore user does not have to
specify any URI for the same but restrictions are named automatically by the system.
5. Creating a new property:
Editor allows creating five different types of properties:
• Annotation property: Since an annotation property cannot have any domain or
range constraints, clicking on the new annotation property button brings up a
dialog that asks the user for information such as the namespace and the annotation
property name.
• Datatype property: A datatype property can have one or more ontology classes
as its domain and one of the pre-defined datatypes as its range. Selecting one or
more classes and clicking on the new Datatype property icon, brings up a window
where the selected classes in the tree are taken as the property’s domain. The
user is then asked to provide information such as the namespace and the property
name. A drop down box allows users to select one of the data types from the list.
• Object, Symmetric and Transitive properties: These properties can have one or
more classes as their domain and range. For a symmetric property the domain and
range are the same. Clicking on any of these options brings up a window where
user is asked to provide information such as the namespace and the property
name. The user is also given two buttons to select one or more classes as values
for domain and range.
6. Removing the selected resources:
All the selected nodes are removed when user clicks on the ‘X’ button. Please note that
since ontology resources are related in various ways, deleting a resource can affect other
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resources in the ontology; for example, deleting a resource can cause other resources
in the same ontology to be deleted too.
7. Searching in ontology:
The Search button allows users to search for resources in the ontology. A window pops
up with an input text field that allows incremental searching. In other words, as user
types in name of the resource, the drop-down list refreshes itself to contain only the
resources that start with the typed string. Selecting one of the resources in this list
and pressing OK, selects the appropriate resource in the editor. The Search function
also allows selecting resources by the property values set on them.
8. Refresh Ontology
The refresh button reloads the ontology and updates the editor.
9. Setting properties on instances/classes:
Right-clicking on an instance brings up a menu that provides a list of properties that
are inherited and applicable to its classes. Selecting a specific property from the menu
allows the user to provide a value for that property. For example, if the property
is an Object property, a new window appears which allows the user to select one or
more instances which are compatible to the range of the selected property. The selected
instances are then set as property values. For classes, all the properties (e.g. annotation
and RDF properties) are listed on the menu.
10. Setting relations among resources:
Two or more classes, or two or more properties, can be set as equivalent; similarly
two or more instances can be marked as the same. Right-clicking on a resource brings
up a menu with an appropriate option (Equivalent Class for ontology classes, Same
As Instance for instances and Equivalent Property for properties) which when clicked
then brings up a window with a drop down box containing a list of resources that the
user can select to specify them as equivalent or the same.
14.6
Ontology Annotation Tool
The Ontology Annotation Tool (OAT) is a GATE plugin available from the Ontology Tools
plugin set, which enables a user to manually annotate a text with respect to one or more
ontologies. The required ontology must be selected from a pull-down list of available ontologies.
The OAT tool supports annotation with information about the ontology classes, instances
and properties.
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Figure 14.8: Viewing Ontology-Based Annotations
14.6.1
Viewing Annotated Text
Ontology-based annotations in the text can be viewed by selecting the desired classes or
instances in the ontology tree in GATE Developer (see Figure 14.8). By default, when a
class is selected, all of its sub-classes and instances are also automatically selected and their
mentions are highlighted in the text. There is an option to disable this default behaviour
(see Section 14.6.4).
Figure 14.8 shows the mentions of each class and instance in a different colour. These colours
can be customised by the user by clicking on the class/instance names in the ontology tree.
It is also possible to expand and collapse branches of the ontology.
14.6.2
Editing Existing Annotations
In order to view the class/instance of a highlighted annotation in the text (e.g., United States
- see Figure 14.9), hover the mouse over it and an edit dialogue will appear. It shows the
current class or instance (Country in our example) and allows the user to delete it or change
it. To delete an existing annotation, press the Delete button.
A class or instance can be changed by starting to type the name of the new class in the
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Figure 14.9: Editing Existing Annotations
combo-box. Then it displays a list of available classes and instances, which start with the
typed string. For example, if we want to change the type from Country to Location, we can
type ‘Lo’ and all classes and instances which names start with Lo will be displayed. The
more characters are typed, the fewer matching classes remain in the list. As soon as one sees
the desired class in the list, it is chosen by clicking on it.
It is possible to apply the changes to all occurrences of the same string and the same previous
class/instance, not just to the current one. This is useful when annotating long texts. The
user needs to make sure that they still check the classes and instances of annotations further
down in the text, in case the same string has a different meaning (e.g., bank as a building
vs. bank as a river bank).
The edit dialogue also allows correcting annotation offset boundaries. In other words, user
can expand or shrink the annotation offsets’ boundaries by clicking on the relevant arrow
buttons.
OAT also allows users to assign property values as annotation features to the existing class
and instance annotations. In the case of class annotation, all annotation properties from the
ontology are displayed in the table. In the case of instance annotations, all properties from
the ontology applicable to the selected instance are shown in the table. The table also shows
existing features of the selected annotation. User can then add, delete or edit any value(s)
of the selected feature. In the case of a property, user is allowed to provide an arbitrary
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Figure 14.10: Add New Annotation
number of values. User can, by clicking on the editList button, add, remove or edit any
value to the property. In case of object properties, users are only allowed to select values
from a pre-selected list of values (i.e. instances which satisfy the selected property’s range
constraints).
14.6.3
Adding New Annotations
New annotations can be added in two ways: using a dialogue (see Figure 14.10) or by
selecting the text and clicking on the desired class or instance in the ontology tree.
When adding a new annotation using the dialogue, select a text and after a very short while,
if the mouse is not moved, a dialogue will appear (see Figure 14.10). Start typing the name
of the desired class or instance, until you see it listed in the combo-box, then select it with
the mouse. This operation is the same, as in changing the class/instance of an existing
annotation. One has the option of applying this choice to the current selection only or to all
mentions of the selected string in the current document (Apply to All check box).
User can also create an instance from the selected text. If user checks the ‘create instance’
checkbox prior to selecting the class, the selected text is annotated with the selected class
and a new instance of the selected class (with the name equivalent to the selected text) is
created (provided there isn’t any existing instance available in the ontology with that name).
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Figure 14.11: Tool Options
14.6.4
Options
There are several options that control the OAT behaviour (see Figure 14.11):
• Disable child feature: By default, when a class is selected, all of its sub-classes are
also automatically selected and their mentions are highlighted in the text. This option
disables that behaviour, so only mentions of the selected class are highlighted.
• Delete confirmation: By default, OAT deletes ontological information without asking for confirmation, when the delete button is pressed. However, if this leads to too
many mistakes, it is possible to enable delete confirmations from this option.
• Disable Case-Sensitive Feature: When user decides to annotate all occurrences
of the selected text (‘apply to all’ option) in the document and if the ‘disable casesensitive feature’ is selected, the tool, when searching for the identical strings in the
document text, ignores the case-sensitivity.
• Setting up a filter to disable resources from the OAT GUI: When user wants
to annotate the text of a document with certain classes/instances of the ontology, s/he
may disable the resources which s/he is not going to use. This option allows users to
select a file which contains class or instance names, one per line. These names are case
sensitive. After selecting a file, when user turns on the ‘filter’ check box, the resources
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specified in the filter file are disabled and removed from the annotation editor window.
User can also add new resources to this list or remove some or all from the list by right
clicking on the respective resource and by selecting the relevant option. Once modified,
the ‘save’ button allows users to export this list to a file.
• Annotation Set: GATE stores information in annotation sets and OAT allows you
to select which set to use as input and output.
• Annotation Type: By default, this is annotation of type Mention, but that can be
changed to any other name. This option is required because OAT uses Gate annotations
to store and read the ontological data. However, to do that, it needs a type (i.e. name)
so ontology-based annotations can be distinguished easily from other annotations (e.g.
tokens, gazetteer lookups).
14.7
Relation Annotation Tool
This tool is designed to annotate a document with ontology instances and to create relations
between annotations with ontology object properties. It is close and compatible with OAT
but focus on relations between annotations, see section 14.6 for OAT.
To use it you must load the Ontology Tools plugin, load a document and an ontology then
show the document and in the document editor click on the button named ‘RAT-C’ (Relation
Annotation Tool Class view) which will also display the ‘RAT-I’ view (Relation Annotation
Tool Instance view).
14.7.1
Description of the two views
The right vertical view shows the loaded ontologies as trees.
To show/hide the annotations in the document, use the class checkbox. The selection of a
class and the ticking of a checkbox are independent and work the same as in the annotation
sets view.
To change the annotation set used to load/save the annotations, use the drop down list at
the bottom of the vertical view.
To hide/show the classes in the tree in order to decrease the amount of elements displayed,
use the context menu on classes selection. The setting is saved in the user preferences.
The bottom horizontal view shows two tables: one for instances and one for properties. The
instances table shows the instances and their labels for the selected class in the ontology
trees and the properties table shows the properties values for the selected instance in the
instances table.
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Figure 14.12: Relation Annotation Tool vertical and horizontal document views
Two buttons allow to add a new instance from the text selection in the document or as a
new label for the selected instance.
To filter on instance labels, use the filter text field. You can clear the field with the X button
at the end of the field.
You can use ‘Show In Ontology Editor’ on the context menu of an instance in the instance
table. Then in the ontology editor you can add class or object properties.
14.7.2
Create new annotation and instance from text selection
• select a class in the ontology tree at the right
• select some text in the document editor and hover the mouse over it
• use the button ‘New Inst.’ in the view at the bottom
• in the bottom left table you have your new instance
• don’t forget to save your document AND the ontology before to quit
14.7.3
Create new annotation and add label to existing instance
from text selection
• select a class in the ontology tree at the right
• select some text in the document editor and hover the mouse on it
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• if the instances table is empty then clear the filter text field
• select an existing instance in the instances table
• use the button ‘Add to Selected Inst.’ in the view at the bottom
• in the bottom left table you have your new label
• don’t forget to save your document AND the ontology before to quit
14.7.4
Create and set properties for annotation relation
• open an ontology with the ontology editor
• if not existing add at least an object property for one class
• set the domain and range accordingly to the type of annotation relation
• add an instance or label as explained previously for the same class
• in the bottom right table you have the properties for this instance
• click in the ‘Value’ column cell to set the object property
• if the list of choices is empty, add first other instances
• don’t forget to save your document AND the ontology before to quit
14.7.5
Delete instance, label or property
• select one or more instances or properties in their respective table
• right-click on the selection for the context menu and choose an item
14.7.6
Differences with OAT and Ontology Editor
This tool is very close to OAT but without the annotation editor popup and instead a bottom
tables view, with multiple ontologies support, with only instance annotation and no class
annotation.
To make OAT compatible with this tool you must use ‘Mention’ as annotation type, ‘class’
and ‘inst’ as feature names. They are the defaults in OAT. You must also select the same
annotation set in the drop down list at the bottom right corner.
You should enable the option ‘Selected Text As Property Value’ in the Options panel of
OAT. So it will add a label from the selected text for each instance.
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The ontology editor is useful to check that an instance is correctly added to the ontology
and to add new annotation relation as object property.
14.8
Using the ontology API
The following code demonstrates how to use the GATE API to create an instance of the
OWLIM Ontology language resource. This example shows how to use the current version of
the API and ontology implementation.
For an example of using the old API and the backwards compatibility plugin, see 14.9.
1
2
// step 1: initialize GATE
if (! Gate . isInitialized ()) { Gate . init (); }
3
4
5
6
// step 2: load the Ontology plugin that contains the implementation
File ontoHome = new File ( Gate . getPluginsHome () , " Ontology " );
Gate . ge tCreol eRegis ter (). addDirectory ( ontoHome . toURL ());
7
8
9
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// step 3: set the parameters
FeatureMap fm = Factory . newFeatureMap ();
fm . put ( " rdfXmlURL " , urlOfTheOntology );
fm . put ( " baseURI " , theBaseURI );
fm . put ( " mappingsURL " , u r l O f T h e M a p p i n g s F i l e );
// .. any other parameters
14
15
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// step 4: finally create an instance of ontology
Ontology ontology = ( Ontology )
Factory . createResource ( " gate . creole . ontology . impl . sesame . OWLIMOntology " ,
fm );
19
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21
// retrieving a list of top classes
Set < OClass > topClasses = ontology . getOClasses ( true );
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26
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// for all top classes, printing their direct sub classes and print
// their URI or blank node ID in turtle format.
for ( OClass c : topClasses ) {
Set < OClass > dcs = c . getSubClasses ( OConstants . DIRECT_CLOSURE );
for ( OClass sClass : dcs ) {
System . out . println ( sClass . getONodeID (). toTurtle ());
}
}
31
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// creating a new class from a full URI
OURI aURI1 = ontology . createOURI ( " http :// sample . en / owlim # Organization " );
OClass or ganiza tionCl ass = ontology . addOClass ( aURI1 );
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// create a new class from a name and the default name space set for
// the ontology
OURI aURI2 = ontology . cr eateOU RIForN ame ( " someOtherName " );
OClass someOtherClass = ontology . addOClass ( aURI2 );
Working with Ontologies
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40
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// set the label for the class
someOtherClass . setLabel ( " some other name " , OConstants . ENGLISH );
43
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48
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50
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// creating a new Datatype property called name
// with domain set to Organization
// with datatype set to string
URI dURI = new URI ( " http :// sample . en / owlim # Name " , false );
Set < OClass > domain = new HashSet < OClass >();
domain . add ( or ganiza tionCl ass );
DatatypeProperty dp =
ontology . a d d Da t a ty p e Pr o p er t y ( dURI , domain , Datatype . getS tringD ataTyp e ());
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55
// creating a new instance of class organization called IBM
OURI iURI = ontology . createOURI ( " http :// sample . en / owlim # IBM " );
OInstance ibm = Ontology . addOInstance ( iURI , organ izatio nClass );
56
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// assigning a Datatype property, name to ibm
ibm . a d d D a t a t y p e P r o p e r t y V a l u e ( dp , new Literal ( " IBM Corporation " ,
59
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// get all the set values of all Datatype properties on the instance ibm
Set < DatatypeProperty > dps = Ontology . g e t D a t a t y p e P r o p e r t i e s ();
for ( DatatypeProperty dp : dps ) {
List < Literal > values = ibm . g e t D a t a t y p e P r o p e r t y V a l u e s ( dp );
System . out . println ( " DP : " + dp . getOURI ());
for ( Literal l : values ) {
System . out . println ( " Value : " + l . getValue ());
System . out . println ( " Datatype : " + l . getDataType (). getXmlSchemaURI ());
}
}
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// export data to a file in Turtle format
BufferedWriter writer = new BufferedWriter ( new FileWriter ( someFile ));
ontology . writ eOntol ogyDat a ( writer , OConstants . OntologyFormat . TURTLE );
writer . close ();
14.9
Using the ontology API (old version)
The following code demonstrates how to use the GATE API to create an instance of the
OWLIM Ontology language resource. This example shows how to use the API with the
backwards-compatibility plugin Ontology OWLIM2
For how to use the API with the current implementation plugin, see 14.8.
1
2
// step 1: initialize GATE
Gate . init ();
3
4
5
// step 2: load the plugin
File ontoHome = new File ( Gate . getPluginsHome () , " Ontology_OWLIM2 " );
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Working with Ontologies
Gate . ge tCreol eRegis ter (). addDirectory ( ontoHome . toURL ());
7
8
9
10
// step 3: set the parameters
FeatureMap fm = Factory . newFeatureMap ();
fm . put ( " rdfXmlURL " , url - of - the - ontology );
11
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13
14
// step 4: finally create an instance of ontology
Ontology ontology = ( Ontology )
Factory . createResource ( " gate . creole . ontology . owlim . OWLIMOntologyLR " , fm );
15
16
17
// retrieving a list of top classes
Set < OClass > topClasses = ontology . getOClasses ( true );
18
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21
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23
24
25
26
// for all top classes, printing their direct sub classes
Iterator < OClass > iter = topClasses . iterator ();
while ( iter . hasNext ()) {
Set < OClass > dcs = iter . next (). getSubClasses ( OConstants . DIRECT_CLOSURE );
for ( OClass aClass : dcs ) {
System . out . println ( aClass . getURI (). toString ());
}
}
27
28
29
30
31
// creating a new class
// false indicates that it is not an anonymous URI
URI aURI = new URI ( " http :// sample . en / owlim # Organization " , false );
OClass or ganiza tionCl ass = ontology . addOClass ( aURI );
32
33
34
35
36
37
38
39
40
// creating a new Datatype property called name
// with domain set to Organization
// with datatype set to string
URI dURI = new URI ( " http :// sample . en / owlim # Name " , false );
Set < OClass > domain = new HashSet < OClass >();
domain . add ( or ganiza tionCl ass );
DatatypeProperty dp = ontology . a dd D a t at y p eP r o pe r t y ( dURI , domain ,
Datatype . getS tringD ataTyp e ());
41
42
43
44
// creating a new instance of class organization called IBM
URI iURI = new URI ( " http :// sample . en / owlim # IBM " , false );
OInstance ibm = Ontology . addOInstance ( iURI , organ izatio nClass );
45
46
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48
// assigning a Datatype property, name to ibm
ibm . a d d D a t a t y p e P r o p e r t y V a l u e ( dp , new Literal ( " IBM Corporation " ,
dp . getDataType ());
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// get all the set values of all Datatype properties on the instance ibm
Set < DatatypeProperty > dps = Ontology . g e t D a t a t y p e P r o p e r t i e s ();
for ( DatatypeProperty dp : dps ) {
List < Literal > values = ibm . g e t D a t a t y p e P r o p e r t y V a l u e s ( dp );
System . out . println ( " DP : " + dp . getURI (). toString ());
for ( Literal l : values ) {
System . out . println ( " Value : " + l . getValue ());
System . out . println ( " Datatype : "
+ l . getDataType (). getXmlSchemaURI (). toString ()); }
Working with Ontologies
59
333
}
60
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65
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// export data to a file in the ntriples format
BufferedWriter writer = new BufferedWriter ( new FileWriter ( someFile ));
String output = ontology . getOntologyData (
OConstants . O N T O L O G Y _ F O R M A T _ N T R I P L E S );
writer . write ( output );
writer . flush ();
writer . close ();
14.10
Ontology-Aware JAPE Transducer
One of the GATE components that makes use of the ontology support is the JAPE transducer (see Chapter 8). Combining the power of ontologies with JAPE’s pattern matching
mechanisms can ease the creation of applications.
In order to use ontologies with JAPE, one needs to load an ontology in GATE before loading
the JAPE transducer. Once the ontology is known to the system, it can be set as the value
for the optional ontology parameter for the JAPE grammar. Doing so alters slightly the
way the matching occurs when the grammar is executed. If a transducer is ontology-aware
(i.e. it has a value set for the ’ontology’ parameter) it will treat all occurrences of the feature
named class differently from the other features of annotations. The values for the feature
class on any type of annotation will be considered as referring to classes in the ontology as
follows:
• if the class feature value is a valid URI (e.g. http://sample.en/owlim#Organization)
then it is treated as a reference to the class (if any) with that URI in the ontology.
• otherwise, it is treated as a name in the ontology’s default namespace. The default
namespace is prepended to the value to give a URI and the feature is treated as referring
to the class with that URI.
For example, if the default namespace of the ontology is http://gate.ac.uk/example# then
a class feature with the value “Person” refers to the http://gate.ac.uk/example#Person
class in the ontology. If the ontology imports other ontologies then it may be useful to define
templates for the various namespace URIs to avoid excessive repetition. There is an example
of this for the PROTON ontology in section 8.1.5.
In ontology-aware mode the matching between two class values will not be based on simple equality but rather hierarchical compatibility. For example if the ontology contains a
class named ‘Politician’, which is a sub class of the class ‘Person’, then a pattern of
{Entity.class == ‘Person’} will successfully match an annotation of type Entity with
a feature class having the value ‘Politician’. If the JAPE transducer were not ontologyaware, such a test would fail.
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This behaviour allows a larger degree of generalisation when designing a set of rules. Rules
that apply several types of entities mentioned in the text can be written using the most
generic class they apply to and need not be repeated for each subtype of entity. One could
have rules applying to Locations without needing to know whether a particular location
happens to be a country or a city.
If a domain ontology is available at the time of building an application, using it in conjunction
with the JAPE transducers can significantly simplify the set of grammars that need to be
written.
The ontology does not normally affect actions on the right hand side of JAPE rules, but
when Java is used on the right hand side, then the ontology becomes accessible via a local
variable named ontology, which may be referenced from within the right-hand-side code.
In Java code, the class feature should be referenced using the static final variable,
LOOKUP CLASS FEATURE NAME, that is defined in gate.creole.ANNIEConstants.
14.11
Annotating Text with Ontological Information
The ontology-aware JAPE transducer enables the text to be linked to classes in an ontology
by means of annotations. Essentially this means that each annotation can have a class and
ontology feature. To add the relevant class feature to an annotation is very easy: simply
add a feature ‘class’ with the classname as its value. To add the relevant ontology, use
ontology.getURL().
Below is a sample rule which looks for a location annotation and identifies it as a ‘Mention’
annotation with the class ‘Location’ and the ontology loaded with the ontology-aware JAPE
transducer (via the runtime parameter of the transducer).
Rule: Location
({Location}):mention
-->
:mention{
// create the ontology and class features
FeatureMap features = Factory.newFeatureMap();
features.put("ontology", ontology.getURL());
features.put("class", "Location");
// create the new annotation
try {
annotations.add(mentionAnnots.firstNode().getOffset(),
mentionAnnots.lastNode().getOffset(), "Mention", features);
Working with Ontologies
335
}
catch(InvalidOffsetException e) {
throw new JapeException(e);
}
}
14.12
Populating Ontologies
Another typical application that combines the use of ontologies with NLP techniques is
finding mentions of entities in text. The scenario is that one has an existing ontology and
wants to use Information Extraction to populate it with instances whenever entities belonging
to classes in the ontology are mentioned in the input texts.
Let us assume we have an ontology and an IE application that marks the input text with
annotations of type ‘Mention’ having a feature ‘class’ specifying the class of the entity
mentioned. The task we are seeking to solve is to add instances in the ontology for every
Mention annotation.
The example presented here is based on a JAPE rule that uses Java code on the action side
in order to access directly the GATE ontology API:
1
2
3
4
5
6
7
8
Rule : FindEntities
({ Mention }): mention
-->
: mention {
//find the annotation matched by LHS
//we know the annotation set returned
//will always contain a single annotation
Annotation mentionAnn = mentionAnnots . iterator (). next ();
9
10
11
12
13
14
15
16
17
18
//find the class of the mention
String className = ( String ) mentionAnn . getFeatures ().
get ( gate . creole . ANNIEConstants . L O O K U P _ C L A S S _ F E A T U R E _ N A M E );
// should normalize class name and avoid invalid class names here!
OClass aClass = ontology . getOClass ( ontology . cr eateOU RIForN ame ( className ));
if ( aClass == null ) {
System . err . println ( " Error class \" " + className + " \" does not exist ! " );
return ;
}
19
20
21
//find the text covered by the annotation
String theMentionText = gate . Utils . stringFor ( doc , mentionAnn );
22
23
24
25
26
27
28
// when creating a URI from text that came from a document you must take care
// to ensure that the name does not contain any characters that are illegal
// in a URI. The following method does this nicely for English but you may
// want to do your own normalization instead if you have non-English text.
String mentionName = OUtils . toResourceName ( theMentionText );
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Working with Ontologies
// get the property to store mention texts for mention instances
DatatypeProperty prop =
ontology . g e t Da t a ty p e Pr o p er t y ( ontology . crea teOURI ForNam e ( " mentionText " ));
29
30
31
32
OURI mentionURI = ontology . cr eateOU RIForN ame ( mentionName );
// if that mention instance does not already exist, add it
if (! ontology . co ntains OInsta nce ( mentionURI )) {
OInstance inst = ontology . addOInstance ( mentionURI , aClass );
// add the actual mention text to the instance
try {
inst . a d d D a t a t y p e P r o p e r t y V a l u e ( prop ,
new Literal ( theMentionText , OConstants . ENGLISH ));
}
catch ( I n v a l i d V a l u e E x c e p t i o n e ) {
throw new JapeException ( e );
}
}
33
34
35
36
37
38
39
40
41
42
43
44
45
46
}
This will match each annotation of type Mention in the input and assign it to a label
‘mention’. That label is then used in the right hand side to find the annotation that was
matched by the pattern (lines 5–10); the value for the class feature of the annotation is
used to identify the ontological class name (lines 12–14); and the annotation span is used to
extract the text covered in the document (lines 16–26). Once all these pieces of information
are available, the addition to the ontology can be done. First the right class in the ontology
is identified using the class name (lines 28–37) and then a new instance for that class is
created (lines 38–50).
Beside JAPE, another tool that could play a part in this application is the Ontological
Gazetteer, see Section 13.4, which can be useful in bootstrapping the IE application that
finds entity mentions.
The solution presented here is purely pedagogical as it does not address many issues that
would be encountered in a real life application solving the same problem. For instance,
it is naı̈ve to assume that the name for the entity would be exactly the text found in the
document. In many cases entities have several aliases – for example the same person name
can be written in a variety of forms depending on whether titles, first names, or initials are
used. A process of name normalisation would probably need to be employed in order to
make sure that the same entity, regardless of the textual form it is mentioned in, will always
be linked to the same ontology instance.
For a detailed description of the GATE ontology API, please consult the JavaDoc documentation.
Working with Ontologies
14.13
337
Ontology API and Implementation Changes
This section describes the changes in the API and the implementation made in GATE Developer version 5.1. The most important change is that the implementation of the ontology API
has been removed from the GATE core and is now being made available as plugins. Currently
the plugin Ontology OWLIM2 provides the implementation that was present in the GATE core
previously and the plugin Ontology provides a new and upgraded implementation that also
implements some new features that were added to the API. The Ontology OWLIM2 plugin
is intended to provide maximum backwards compatibility but will not be developed further
and be phased out in the future, while the Ontology plugin provides the current actively
developed implementation.
Before any ontology-related function can be used in GATE, one of the ontology implementation plugins must be loaded.
14.13.1
Differences between the implementation plugins
The implementation provided in plugin Ontology OWLIM2 is based on Sesame version 1 and
OWLIM version 2, while the changed implementation provided in plugin Ontology is based
on Sesame version 2 and OWLIM version 3.
The plugin Ontology provides the ontology language resource OWLIM Ontology with new
and changed parameters. In addition, there are two language resources for advanced users,
Create Sesame Ontology and Connect Sesame Ontology. Finally the new implementation provides the language resource OWLIM Ontology DEPRECATED to make the move from
the old to the new implementation easier: this language resource has the same name, parameters and Java package as the language resource OWLIMOntologyLR in backwards-compatibility
plugin Ontology OWLIM2. This allows to test existing pipelines and applications with the
new implementation without the necessity to adapt the names of the language resource or
parameters.
The implementation in plugin Ontology makes various attempts to reduce the amount of
memory needed to load an ontology. This will allow to load significantly larger ontologies
into GATE. This comes at the price of some methods needing more time than before, as the
implementation does not cache all ontology entities in GATE’s memory any more.
The new implementation does not provide access to any implementation detail anymore, the
method getSesameRepository will therefore throw an exception. The return type of this
method in the old implementation has been changed to Object to remove the dependency
on a Sesame class in the GATE API.
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14.13.2
Working with Ontologies
Changes in the Ontology API
The class gate.creole.ontology.URI has been deprecated. Instead, the ontology API
client must use objects that implement the new OURI, OBnodeID or ONodeID interfaces.
An API client can only directly create OURI objects and must use the ontology factory
methods createOURI, createOURIFroName or generateOURI to create such objects.
Also, the intended way how ontologies are modeled has been changed: the API tries to
prevent a user from adding anything to an ontology that would make an ontology that
conforms to OWL-Lite go beyond that sublanguage (and e.g. become OWL-Full). However,
if an ontology already is not conforming to OWL-Lite, the API tries to make as much
information visible to the client as possible. That means for instance that RDF classes will
be included in the list of classes returned by method getOClasses, but there is no support
for adding RDF classes to an ontology. Similarly, all methods that already existed which
would allow to add entities to an ontology that do not conform to OWL-Lite have been
deprecated.
Most methods that use a constant from class OConstants which is defined as a byte value
have been deprecated and replaced by methods that use enums that replace the byte constants instead. (however, the byte constants used for literal string languages are still used).
The API now supports the handling of ontology imports more flexibly. Ontology imports
are internally kept in a named graph that is different from the named graph data from
loaded ontologies is kept in. Imported ontology data is still visible to the ontology API but
can be ignored when storing (serializing) an ontology. The ontology API now also allows
to explicitly resolve ontology imports and it allows the specification of mappings between
import URIs and URLs of either local files or substitute web URLs. The import map can
also specify patterns for substituting import URIs with replacement URLs (or ignoring them
altogether).
The default namespace URI is now set automatically from the ontology if possible and the
API allows getting and setting the ontology URI.
The ontology API now offers methods for getting an iterator when accessing some ontology
resources, e.g. when getting all classes in the ontology. This helps to prevent the excessive
use of memory when retrieving a large number of such resources from a large ontology.
Ontology objects do not internally store copies of all ontology resources in hash maps
any more. This means that re-fetching ontology resources will be a slower operation and
old methods that rely on this mechanism are either deprecated (getOResourcesByName,
getOResourceByName) or do not work at all any more (getOResourceFromMap,
addOResourceToMap, removeOResourceFromMap).
Chapter 15
Non-English Language Support
There are plugins available for processing the following languages: French, German, Spanish,
Italian, Chinese, Arabic, Romanian, Hindi and Cebuano. Some of the applications are quite
basic and just contain some useful processing resources to get you started when developing
a full application. Others (Cebuano and Hindi) are more like toy systems built as part of an
exercise in language portability.
Note that if you wish to use individual language processing resources without loading the
whole application, you will need to load the relevant plugin for that language in most cases.
The plugins all follow the same kind of format. Load the plugin using the plugin manager
in GATE Developer, and the relevant resources will be available in the Processing Resources
set.
Some plugins just contain a list of resources which can be added ad hoc to other applications.
For example, the Italian plugin simply contains a lexicon which can be used to replace the
English lexicon in the default English POS tagger: this will provide a reasonable basic POS
tagger for Italian.
In most cases you will also find a directory in the relevant plugin directory called data which
contains some sample texts (in some cases, these are annotated with NEs).
There are also a number of plugins, documented elsewhere in this manual that while they
default to processing English can be configured to support other languages. These include
the TaggerFramework (Section 20.3), the Numbers Tagger (Section 20.5.1), and the Snowball
based stemmer (Section 20.9). The LingPipe POS Tagger PR (Section 20.25.3) now includes
two models for Bulgarian.
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340
15.1
Non-English Language Support
French Plugin
The French plugin contains two applications for NE recognition: one which includes the
TreeTagger for POS tagging in French (french+tagger.gapp) , and one which does not
(french.gapp). Simply load the application required from the plugins/Lang French directory.
You do not need to load the plugin itself from the GATE Developer’s Plugin Management
Console. Note that the TreeTagger must first be installed and set up correctly (see Section
20.3 for details). Check that the runtime parameters are set correctly for your TreeTagger in your application. The applications both contain resources for tokenisation, sentence
splitting, gazetteer lookup, NE recognition (via JAPE grammars) and orthographic coreference. Note that they are not intended to produce high quality results, they are simply a
starting point for a developer working on French. Some sample texts are contained in the
plugins/Lang French/data directory.
15.2
German Plugin
The German plugin contains two applications for NE recognition: one which includes the
TreeTagger for POS tagging in German (german+tagger.gapp) , and one which does not (german.gapp). Simply load the application required from the plugins/Lang German/resources
directory. You do not need to load the plugin itself from the GATE Developer’s Plugin
Management Console. Note that the TreeTagger must first be installed and set up correctly (see Section 20.3 for details). Check that the runtime parameters are set correctly
for your TreeTagger in your application. The applications both contain resources for tokenisation, sentence splitting, gazetteer lookup, compound analysis, NE recognition (via JAPE
grammars) and orthographic coreference. Some sample texts are contained in the plugins/Lang German/data directory. We are grateful to Fabio Ciravegna and the Dot.KOM
project for use of some of the components for the German plugin.
15.3
Romanian Plugin
The Romanian plugin contains an application for Romanian NE recognition (romanian.gapp). Simply load the application from the plugins/Lang Romanian/resources directory. You do not need to load the plugin itself from the GATE Developer’s Plugin
Management Console. The application contains resources for tokenisation, gazetteer lookup,
NE recognition (via JAPE grammars) and orthographic coreference. Some sample texts are
contained in the plugins/romanian/corpus directory.
Non-English Language Support
15.4
341
Arabic Plugin
The Arabic plugin contains a simple application for Arabic NE recognition (arabic.gapp).
Simply load the application from the plugins/Lang Arabic/resources directory. You do not
need to load the plugin itself from the GATE Developer’s Plugin Management Console. The
application contains resources for tokenisation, gazetteer lookup, NE recognition (via JAPE
grammars) and orthographic coreference. Note that there are two types of gazetteer used in
this application: one which was derived automatically from training data (Arabic inferred
gazetteer), and one which was created manually. Note that there are some other applications
included which perform quite specific tasks (but can generally be ignored). For example,
arabic-for-bbn.gapp and arabic-for-muse.gapp make use of a very specific set of training
data and convert the result to a special format. There is also an application to collect new
gazetteer lists from training data (arabic lists collector.gapp). For details of the gazetteer
list collector please see Section 13.8.
15.5
Chinese Plugin
The Chinese plugin contains two components: a simple application for Chinese NE recognition (chinese.gapp) and a component called “Chinese Segmenter”.
In order to use the former, simply load the application from the plugins/Lang Chinese/resources
directory. You do not need to load the plugin itself from the GATE Developer’s Plugin Management Console. The application contains resources for tokenisation, gazetteer lookup, NE
recognition (via JAPE grammars) and orthographic coreference. The application makes
use of some gazetteer lists (and a grammar to process them) derived automatically from
training data, as well as regular hand-crafted gazetteer lists. There are also applications
(listscollector.gapp, adj collector.gapp and nounperson collector.gapp) to create such lists,
and various other application to perform special tasks such as coreference evaluation (coreference eval.gapp) and converting the output to a different format (ace-to-muse.gapp).
15.5.1
Chinese Word Segmentation
Unlike English, Chinese text does not have a symbol (or delimiter) such as blank space to
explicitly separate a word from the surrounding words. Therefore, for automatic Chinese
text processing, we may need a system to recognise the words in Chinese text, a problem
known as Chinese word segmentation. The plugin described in this section performs the
task of Chinese word segmentation. It is based on our work using the Perceptron learning
algorithm for the Chinese word segmentation task of the Sighan 20051 . [Li et al. 05c]. Our
Perceptron based system has achieved very good performance in the Sighan-05 task.
1
See http://www.sighan.org/bakeoff2005/ for the Sighan-05 task
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Non-English Language Support
The plugin is called Lang Chinese and is available in the GATE distribution. The corresponding processing resource’s name is Chinese Segmenter PR. Once you load the PR into
GATE, you may put it into a Pipeline application. Note that it does not process a corpus
of documents, but a directory of documents provided as a parameter (see description of
parameters below). The plugin can be used to learn a model from segmented Chinese text
as training data. It can also use the learned model to segment Chinese text. The plugin
can use different learning algorithms to learn different models. It can deal with different
character encodings for Chinese text, such as UTF-8, GB2312 or BIG5. These options can
be selected by setting the run-time parameters of the plugin.
The plugin has five run-time parameters, which are described in the following.
• learningAlg is a String variable, which specifies the learning algorithm used for producing the model. Currently it has two values, PAUM and SVM, representing the two
popular learning algorithms Perceptron and SVM, respectively. The default value is
PAUM.
Generally speaking, SVM may perform better than Perceptron, in particular for small
training sets. On the other hand, Perceptron’s learning is much faster than SVM’s.
Hence, if you have a small training set, you may want to use SVM to obtain a better
model. However, if you have a big training set which is typical for the Chinese word
segmentation task, you may want to use Perceptron for learning, because the SVM’s
learning may take too long time. In addition, using a big training set, the performance
of the Perceptron model is quite similar to that of the SVM model. See [Li et al. 05c]
for the experimental comparison of SVM and Perceptron on Chinese word segmentation.
• learningMode determines the two modes of using the plugin, either learning a model
from training data or applying a learned model to segment Chinese text. Accordingly it
has two values, SEGMENTING and LEARNING. The default value is SEGMENTING,
meaning segmenting the Chinese text.
Note that you first need to learn a model and then you can use the learned model to
segment the text. Several models using the training data used in the Sighan-05 Bakeoff
are available for this plugin, which you can use to segment your Chinese text. More
descriptions about the provided models will be given below.
• modelURL specifies an URL referring to a directory containing the model. If the
plugin is in the LEARNING runmode, the model learned will be put into the directory.
If it is in the SEGMENTING runmode, the plugin will use the model stored in the
directory to segment the text. The models learned from the Sighan-05 bakeoff training
data will be discussed below.
• textCode specifies the encoding of the text used. For example it can be UTF-8, BIG5,
GB2312 or any other encoding for Chinese text. Note that, when you segment some
Chinese text using a learned model, the Chinese text should use the same encoding as
the one used by the training text for obtaining the model.
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• textFilesURL specifies an URL referring to a directory containing the Chinese documents. All the documents contained in this directory (but not those documents contained in its sub-directory if there is any) will be used as input data. In the LEARNING
runmode, those documents contain the segmented Chinese text as training data. In
the SEGMENTING runmode, the text in those documents will be segmented. The
segmented text will be stored in the corresponding documents in the sub-directory
called segmented.
The following PAUM models are distributed with plugins and are available as compressed zip
files under the plugins/Lang Chinese/resources/models directory. Please unzip them to use.
In detail, those models were learned using the PAUM learning algorithm from the corpora
provided by Sighan-05 bakeoff task.
• the PAUM model learned from PKU training data, using the PAUM learning algorithm
and the UTF-8 encoding, is available as model-paum-pku-utf8.zip.
• the PAUM model learned from PKU training data, using the PAUM learning algorithm
and the GB2312 encoding, is available as model-paum-pku-gb.zip.
• the PAUM model learned from AS training data, using the PAUM learning algorithm
and the UTF-8 encoding, is available as model-as-utf8.zip.
• the PAUM model learned from AS training data, using the PAUM learning algorithm
and the BIG5 encoding, is available as model-as-big5.zip.
As you can see, those models were learned using different training data and different Chinese
text encodings of the same training data. The PKU training data are news articles published
in mainland China and use simplified Chinese, while the AS training data are news articles
published in Taiwan and use traditional Chinese. If your text are in simplified Chinese, you
can use the models trained by the PKU data. If your text are in traditional Chinese, you
need to use the models trained by the AS data. If your data are in GB2312 encoding or any
compatible encoding, you need use the model trained by the corpus in GB2312 encoding.
Note that the segmented Chinese text (either used as training data or produced by this
plugin) use the blank space to separate a word from its surrounding words. Hence, if your
data are in Unicode such as UTF-8, you can use the GATE Unicode Tokeniser to process the
segmented text to add the Token annotations into your text to represent the Chinese words.
Once you get the annotations for all the Chinese words, you can perform further processing
such as POS tagging and named entity recognition.
15.6
Hindi Plugin
The Hindi plugin (‘Lang Hindi’) contains a set of resources for basic Hindi NE recognition
which mirror the ANNIE resources but are customised to the Hindi language. You need to
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Non-English Language Support
have the ANNIE plugin loaded first in order to load any of these PRs. With the Hindi, you
can create an application similar to ANNIE but replacing the ANNIE PRs with the default
PRs from the plugin.
Chapter 16
Parsers
16.1
MiniPar Parser
MiniPar is a shallow parser. In its shipped version, it takes one sentence as an input and
determines the dependency relationships between the words of a sentence. It parses the
sentence and brings out the information such as:
• the lemma of the word;
• the part of speech of the word;
• the head modified by this word;
• name of the dependency relationship between this word and the head;
• the lemma of the head.
In the version of MiniPar integrated in GATE (‘Parser Minipar’ plugin), it generates annotations of type ‘DepTreeNode’ and the annotations of type ‘[relation]’ that exists between the
head and the child node. The document is required to have annotations of type ‘Sentence’,
where each annotation consists of a string of the sentence.
Minipar takes one sentence at a time as an input and generates the tokens of type ‘DepTreeNode’. Later it assigns relation between these tokens. Each DepTreeNode consists of
feature called ‘word’: this is the actual text of the word.
For each and every annotation of type ‘[Rel]’, where ‘Rel’ is obj, pred etc. This is the name of
the dependency relationship between the child word and the head word (see Section 16.1.5).
Every ‘[Rel]’ annotation is assigned four features:
• child word: this is the text of the child annotation;
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Figure 16.1: a MiniPar annotated document
• child id: IDs of the annotations which modify the current word (if any).
• head word: this is the text of the head annotation;
• head id: ID of the annotation modified by the child word (if any);
Figure 16.1 shows a MiniPar annotated document in GATE Developer.
16.1.1
Platform Supported
MiniPar in GATE is supported for the Linux and Windows operating systems. Trying to
instantiate this PR on any other OS will generate the ResourceInstantiationException.
Parsers
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Resources
MiniPar in GATE is shipped with four basic resources:
• MiniparWrapper.jar: this is a JAVA Wrapper for MiniPar;
• creole.XML: this defines the required parameters for MiniPar Wrapper;
• minipar.linux: this is a modified version of pdemo.cpp.
• minipar-windows.exe : this is a modified version of pdemo.cpp compiled to work on
windows.
16.1.3
Parameters
The MiniPar wrapper takes six parameters:
• annotationTypeName: new annotations are created with this type, default is ”DepTreeNode”;
• annotationInputSetName: annotations of Sentence type are provided as an input
to MiniPar and are taken from the given annotationSet;
• annotationOutputSetName: All annotations created by Minipar Wrapper are
stored under the given annotationOutputSet;
• document: the GATE document to process;
• miniparBinary: location of the MiniPar Binary file (i.e. either minipar.linux or
minipar-windows.exe. These files are available under gate/plugins/minipar/ directory);
• miniparDataDir: location of the ‘data’ directory under the installation directory of
MINIPAR. default is ”%MINIPAR HOME%/data”.
16.1.4
Prerequisites
The MiniPar wrapper requires the MiniPar library to be available on the underlying Linux/Windows machine. It can be downloaded from the MiniPar homepage.
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16.1.5
Parsers
Grammatical Relationships
appo
"ACME president, --appo-> P.W. Buckman"
aux "should <-aux-- resign"
be "is <-be-- sleeping"
c
"that <-c-- John loves Mary"
comp1
first complement
det "the <-det ‘-- hat"
gen "Jane’s <-gen-- uncle"
i
the relationship between a C clause and its I clause
inv-aux
inverted auxiliary: "Will <-inv-aux-- you stop it?"
inv-be
inverted be: "Is <-inv-be-- she sleeping"
inv-have
inverted have: "Have <-inv-have-- you slept"
mod the relationship between a word and its adjunct modifier
pnmod
post nominal modifier
p-spec
specifier of prepositional phrases
pcomp-c
clausal complement of prepositions
pcomp-n
nominal complement of prepositions
post
post determiner
pre
pre determiner
pred
predicate of a clause
rel
relative clause
vrel
passive verb modifier of nouns
wha, whn, whp: wh-elements at C-spec positions
obj
object of verbs
obj2
second object of ditransitive verbs
subj
subject of verbs
s
surface subjec
16.2
RASP Parser
RASP (Robust Accurate Statistical Parsing) is a robust parsing system for English, developed by the Natural Language and Computational Linguistics group at the University of
Sussex.
This plugin, ‘Parser RASP’, developed by DigitalPebble, provides four wrapper PRs that
call the RASP modules as external programs, as well as a JAPE component that translates
the output of the ANNIE POS Tagger (Section 6.6).
RASP2 Tokenizer This PR requires Sentence annotations and creates Token annotations with a string feature. Note that sentence-splitting must be carried out before
tokenization; the the RegEx Sentence Splitter (see Section 6.5) is suitable for this.
(Alternatively, you can use the ANNIE Tokenizer (Section 6.2) and then the ANNIE
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Sentence Splitter (Section 6.4); their output is compatible with the other PRs in this
plugin).
RASP2 POS Tagger This requires Token annotations and creates WordForm annotations
with pos, probability, and string features.
RASP2 Morphological Analyser This requires WordForm annotations (from the POS
Tagger) and adds lemma and suffix features.
RASP2 Parser This requires the preceding annotation types and creates multiple
Dependency annotations to represent a parse of each sentence.
RASP POS Converter This PR requires Token annotations with a category feature as
produced by the ANNIE POS Tagger (see Section 6.6 and creates WordForm annotations
in the RASP Format. The ANNIE POS Tagger and this Converter can together be
used as a substitute for the RASP2 POS Tagger.
Here are some examples of corpus pipelines that can be correctly constructed with these
PRs.
1. RegEx Sentence Splitter
2. RASP2 Tokenizer
3. RASP2 POS Tagger
4. RASP2 Morphological Analyser
5. RASP2 Parser
1. RegEx Sentence Splitter
2. RASP2 Tokenizer
3. ANNIE POS Tagger
4. RASP POS Converter
5. RASP2 Morphological Analyser
6. RASP2 Parser
1. ANNIE Tokenizer
2. ANNIE Sentence Splitter
3. RASP2 POS Tagger
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4. RASP2 Morphological Analyser
5. RASP2 Parser
1. ANNIE Tokenizer
2. ANNIE Sentence Splitter
3. ANNIE POS Tagger
4. RASP POS Converter
5. RASP2 Morphological Analyser
6. RASP2 Parser
Further documentation is included in the directory gate/plugins/Parser\_RASP/doc/.
The RASP package, which provides the external programs, is available from the RASP web
page.
RASP is only supported for Linux operating systems. Trying to run it on any other operating
systems will generate an exception with the message: ‘The RASP cannot be run on any other
operating systems except Linux.’
It must be correctly installed on the same machine as GATE, and must be installed in a
directory whose path does not contain any spaces (this is a requirement of the RASP scripts
as well as the wrapper). Before trying to run scripts for the first time, edit rasp.sh and
rasp_parse.sh to set the correct value for the shell variable RASP, which should be the file
system pathname where you have installed the RASP tools (for example, RASP=/opt/RASP or
RASP=/usr/local/RASP. You will need to enter the same path for the initialization parameter
raspHome for the POS Tagger, Morphological Analyser, and Parser PRs.
(On some systems the arch command used in the scripts is not available; a work-around is
to comment that line out and add arch=’ix86_linux’, for example.)
(The previous version of the RASP plugin can now be found in plugins/Obsolete/rasp.)
16.3
SUPPLE Parser
SUPPLE is a bottom-up parser that constructs syntax trees and logical forms for English
sentences. The parser is complete in the sense that every analysis licensed by the grammar
is produced. In the current version only the ‘best’ parse is selected at the end of the parsing
process. The English grammar is implemented as an attribute-value context free grammar
which consists of subgrammars for noun phrases (NP), verb phrases (VP), prepositional
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phrases (PP), relative phrases (R) and sentences (S). The semantics associated with each
grammar rule allow the parser to produce logical forms composed of unary predicates to
denote entities and events (e.g., chase(e1), run(e2)) and binary predicates for properties
(e.g. lsubj(e1,e2)). Constants (e.g., e1, e2) are used to represent entity and event identifiers.
The GATE SUPPLE Wrapper stores syntactic information produced by the parser in the
gate document in the form of parse annotations containing a bracketed representation of the
parse; and semantics annotations that contains the logical forms produced by the parser.
It also produces SyntaxTreeNode annotations that allow viewing of the parse tree for a
sentence (see Section 16.3.4).
16.3.1
Requirements
The SUPPLE parser is written in Prolog, so you will need a Prolog interpreter to run
the parser. A copy of PrologCafe (http://kaminari.scitec.kobe-u.ac.jp/PrologCafe/), a
pure Java Prolog implementation, is provided in the distribution. This should work on
any platform but it is not particularly fast. SUPPLE also supports the open-source
SWI Prolog (http://www.swi-prolog.org) and the commercially licenced SICStus prolog
(http://www.sics.se/sicstus, SUPPLE supports versions 3 and 4), which are available for
Windows, Mac OS X, Linux and other Unix variants. For anything more than the simplest
cases we recommend installing one of these instead of using PrologCafe.
16.3.2
Building SUPPLE
The SUPPLE plugin must be compiled before it can be used, so you will require a suitable
Java SDK (GATE itself requires only the JRE to run). To build SUPPLE, first edit the file
build.xml in the Parser SUPPLE directory under plugins, and adjust the user-configurable
options at the top of the file to match your environment. In particular, if you are using SWI
or SICStus Prolog, you will need to change the swi.executable or sicstus.executable
property to the correct name for your system. Once this is done, you can build the plugin
by opening a command prompt or shell, going to the Parser SUPPLE directory and running:
../../bin/ant swi
(on Windows, use ..\..\bin\ant). For PrologCafe or SICStus, replace swi with plcafe or
sicstus as appropriate.
16.3.3
Running the Parser in GATE
In order to parse a document you will need to construct an application that has:
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• tokeniser
• splitter
• POS-tagger
• Morphology
• SUPPLE Parser with parameters
mapping file (config/mapping.config)
feature table file (config/feature table.config)
parser file (supple.plcafe or supple.sicstus or supple.swi)
prolog implementation (shef.nlp.supple.prolog.PrologCafe,
shef.nlp.supple.prolog.SICStusProlog3, shef.nlp.supple.prolog.SICStusProlog4,
shef.nlp.supple.prolog.SWIProlog or shef.nlp.supple.prolog.SWIJavaProlog1 ).
You can take a look at build.xml to see examples of invocation for the different implementations.
Note that prior to GATE 3.1, the parser file parameter was of type java.io.File. From
3.1 it is of type java.net.URL. If you have a saved application (.gapp file) from before
GATE 3.1 which includes SUPPLE it will need to be updated to work with the new version.
Instructions on how to do this can be found in the README file in the SUPPLE plugin
directory.
16.3.4
Viewing the Parse Tree
GATE Developer provides a syntax tree viewer in the Tools plugin which can display the
parse tree generated by SUPPLE for a sentence. To use the tree viewer, be sure that the
Tools plugin is loaded, then open a document in GATE Developer that has been processed
with SUPPLE and view its Sentence annotations. Right-click on the relevant Sentence
annotation in the annotations table and select ‘Edit with syntax tree viewer’. This viewer
can also be used with the constituency output of the Stanford Parser PR (Section 16.4).
16.3.5
System Properties
The SICStusProlog (3 and 4) and SWIProlog implementations work by calling the native
prolog executable, passing data back and forth in temporary files. The location of the prolog
executable is specified by a system property:
1
shef.nlp.supple.prolog.SICStusProlog exists for backwards compatibility and behaves the same as SICStusProlog3.
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• for SICStus: supple.sicstus.executable - default is to look for sicstus.exe (Windows) or sicstus (other platforms) on the PATH.
• for SWI: supple.swi.executable - default is to look for plcon.exe (Windows) or
swipl (other platforms) on the PATH.
If your prolog is installed under a different name, you should specify the correct name in
the relevant system property. For example, when installed from the source distribution, the
Unix version of SWI prolog is typically installed as pl, most binary packages install it as
swipl, though some use the name swi-prolog. You can also use the properties to specify
the full path to prolog (e.g. /opt/swi-prolog/bin/pl) if it is not on your default PATH.
For details of how to pass system properties to GATE, see the end of Section 2.3.
16.3.6
Configuration Files
Two files are used to pass information from GATE to the SUPPLE parser: the mapping file
and the feature table file.
Mapping File
The mapping file specifies how annotations produced using GATE are to be passed to the
parser. The file is composed of a number of pairs of lines, the first line in a pair specifies
a GATE annotation we want to pass to the parser. It includes the AnnotationSet (or
default), the AnnotationType, and a number of features and values that depend on the
AnnotationType. The second line of the pair specifies how to encode the GATE annotation
in a SUPPLE syntactic category, this line also includes a number of features and values. As
an example consider the mapping:
Gate;AnnotationType=Token;category=DT;string=&S
SUPPLE;category=dt;m_root=&S;s_form=&S
It specifies how a determinant (’DT’) will be translated into a category ‘dt’ for the parser.
The construct ‘&S’ is used to represent a variable that will be instantiated to the appropriate
value during the mapping process. More specifically a token like ‘The’ recognised as a DT
by the POS-tagging will be mapped into the following category:
dt(s_form:’The’,m_root:’The’,m_affix:’_’,text:’_’).
As another example consider the mapping:
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Gate;AnnotationType=Lookup;majorType=person_first;minorType=female;string=&S
SUPPLE;category=list_np;s_form=&S;ne_tag=person;ne_type=person_first;gender=female
It specified that an annotation of type ‘Lookup’ in GATE is mapped into a category ‘list np’
with specific features and values. More specifically a token like ‘Mary’ identified in GATE
as a Lookup will be mapped into the following SUPPLE category:
list_np(s_form:’Mary’,m_root:’_’,m_affix:’_’,
text:’_’,ne_tag:’person’,ne_type:’person_first’,gender:’female’).
Feature Table
The feature table file specifies SUPPLE ‘lexical’ categories and its features. As an example
an entry in this file is:
n;s_form;m_root;m_affix;text;person;number
which specifies which features and in which order a noun category should be written. In this
case:
n(s_form:...,m_root:...,m_affix:...,text:...,person:...,number:....).
16.3.7
Parser and Grammar
The parser builds a semantic representation compositionally, and a ‘best parse’ algorithm
is applied to each final chart, providing a partial parse if no complete sentence span can be
constructed. The parser uses a feature valued grammar. Each Category entry has the form:
Category(Feature1:Value1,...,FeatureN:ValueN)
where the number and type of features is dependent on the category type (see Section 5.1).
All categories will have the features s form (surface form) and m root (morphological root);
nominal and verbal categories will also have person and number features; verbal categories
will also have tense and vform features; and adjectival categories will have a degree feature.
The list np category has the same features as other nominal categories plus ne tag and
ne type.
Syntactic rules are specified in Prolog with the predicate rule(LHS, RHS) where LHS is a
syntactic category and RHS is a list of syntactic categories. A rule such as BN P HEAD ⇒
N (‘a basic noun phrase head is composed of a noun’) is written as follows:
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rule(bnp_head(sem:E^[[R,E],[number,E,N]],number:N),
[n(m_root:R,number:N)]).
where the feature ‘sem’ is used to construct the semantics while the parser processes input,
and E, R, and N are variables to be instantiated during parsing.
The full grammar of this distribution can be found in the prolog/grammar directory, the file
load.pl specifies which grammars are used by the parser. The grammars are compiled when
the system is built and the compiled version is used for parsing.
16.3.8
Mapping Named Entities
SUPPLE has a prolog grammar which deals with named entities, the only information required is the Lookup annotations produced by Gate, which are specified in the mapping file.
However, you may want to pass named entities identified with your own Jape grammars in
GATE. This can be done using a special syntactic category provided with this distribution.
The category sem cat is used as a bridge between Gate named entities and the SUPPLE
grammar. An example of how to use it (provided in the mapping file) is:
Gate;AnnotationType=Date;string=&S
SUPPLE;category=sem_cat;type=Date;text=&S;kind=date;name=&S
which maps a named entity ‘Date’ into a syntactic category ’sem cat’. A grammar file
called semantic rules.pl is provided to map sem cat into the appropriate syntactic category
expected by the phrasal rules. The following rule for example:
rule(ne_np(s_form:F,sem:X^[[name,X,NAME],[KIND,X]]),[
sem_cat(s_form:F,text:TEXT,type:’Date’,kind:KIND,name:NAME)]).
is used to parse a ‘Date’ into a named entity in SUPPLE which in turn will be parsed into
a noun phrase.
16.3.9
Upgrading from BuChart to SUPPLE
In theory upgrading from BuChart to SUPPLE should be relatively straightforward. Basically any instance of BuChart needs to be replaced by SUPPLE. Specific changes which
must be made are:
• The compiled parser files are now supple.swi, supple.sicstus, or supple.plcafe
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• The GATE wrapper parameter buchartFile is now SUPPLEFile, and it is now of type
java.net.URL rather than java.io.File. Details of how to compensate for this in
existing saved applications are given in the SUPPLE README file.
• The Prolog wrappers now start shef.nlp.supple.prolog instead of shef.nlp.buchart.prolog
• The mapping.conf file now has lines starting SUPPLE; instead of Buchart;
• Most importantly the main wrapper class is now called nlp.shef.supple.SUPPLE
Making these changes to existing code should be trivial and allow application to benefit from
future improvements to SUPPLE.
16.4
Stanford Parser
The Stanford Parser is a probabilistic parsing system implemented in Java by Stanford
University’s Natural Language Processing Group. Data files are available from Stanford for
parsing Arabic, Chinese, English, and German.
This plugin, ‘Parser Stanford’, developed by the GATE team, provides a PR
(gate.stanford.Parser) that acts as a wrapper around the Stanford Parser (version 1.6.5)
and translates GATE annotations to and from the data structures of the parser itself. The
plugin is supplied with the unmodified jar file and one English data file obtained from
Stanford. Stanford’s software itself is subject to the full GPL.
The parser itself can be trained on other corpora and languages, as documented on the
website, but this plugin does not provide a means of doing so. Trained data files are not necessarily compatible between different versions of the parser; in particular files from versions
before 1.6.1 are always incompatible with the software from version 1.6.1 onwards. (GATE
switched from 1.6 to 1.6.1 at build 3120 in January 2009, and to 1.6.5 in December 2010.)
Creating multiple instances of this PR in the same JVM—even with the same language
model file—does not work. The PRs can be instantiated, but runtime errors will almost
certainly occur. In particular, this PR is not safe for use in a multithreaded environment
(see Section 7.13).
16.4.1
Input Requirements
Documents to be processed by the Parser PR must already have Sentence and Token annotations, such as those produced by either ANNIE Sentence Splitter (Sections 6.4 and 6.5)
and the ANNIE English Tokeniser (Section 6.2).
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If the reusePosTags parameter is true, then the Token annotations must have category
features with compatible POS tags. The tags produced by the ANNIE POS Tagger are
compatible with Stanford’s parser data files for English (which also use the Penn treebank
tagset).
16.4.2
Initialization Parameters
parserFile the path to the trained data file; the default value points to the English data
file2 included with the GATE distribution. You can also use other files downloaded
from the Stanford Parser website or produced by training the parser.
mappingFile the optional path to a mapping file: a flat, two-column file which the wrapper
can use to ‘translate’ tags. A sample file is included.3 By default this value is null
and mapping is ignored.
tlppClass an implementation of TreebankLangParserParams, used by the parser itself to
extract the dependency relations from the constituency structures. The default value
is compatible with the English data file supplied. Please refer to the Stanford NLP
Group’s documentation and the parser’s javadoc for a further explanation.
16.4.3
Runtime Parameters
annotationSetName the name of the annotationSet used for input (Token and Sentence
annotations) and output (SyntaxTreeNode and Dependency annotations, and
category and dependencies features added to Tokens).
debug a boolean value which controls the verbosity of the wrapper’s output.
reusePosTags if true, the wrapper will read category features (produced by an earlier
POS-tagging PR) from the Token annotations and force the parser to use them.
useMapping if this is true and a mapping file was loaded when the PR was initialized, the
POS and syntactic tags produced by the parser will be translated using that file. If no
mapping file was loaded, this parameter is ignored.
The following boolean parameters switch on and off the various types of output that the
parser can produce. Any or all of them can be true, but if all are false the PR will simply
print a warning to save time (instead of running the parser).
addPosTags if this is true, the wrapper will add category features to the Token annotations.
2
3
resources/englishPCFG.ser.gz
resources/english-tag-map.txt
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addConstituentAnnotations if true, the wrapper will mark the syntactic constituents
with SyntaxTreeNode annotations that are compatible with the Syntax Tree Viewer
(see Section 16.3.4).
addDependencyAnnotations if true, the wrapper will add Dependency annotations to
indicate the dependency relations in the sentence.
addDependencyFeatures if true, the wrapper will add dependencies features to the
Token annotations to indicate the dependency relations in the sentence.
The parser will derive the dependency structures only if either or both of the dependency
output options is enabled, so if you do not need the dependency analysis, you can disable
both of them and the PR will run faster.
Two sample GATE applications for English are included in the plugins/Parser Stanford
directory: sample_parser_en.gapp runs the Regex Sentence Splitter and ANNIE Tokenizer and then this PR to annotate constituency and dependency structures, whereas
sample_pos+parser_en.gapp also runs the ANNIE POS Tagger and makes the parser re-use
its POS tags.
Chapter 17
Machine Learning
This chapter presents machine learning PRs available in GATE. Currently, two PRs are
available:
• The Batch Learning PR (in the Learning plugin) is GATE’s most comprehensive
and developed machine learning offering. It is specifically targetted at NLP tasks
including text classification, chunk learning (e.g. for named entity recognition) and
relation learning. It integrates LibSVM for improved speed, along with the PAUM
algorithm, offering very competitive performance and speed. It also offers a Weka
interface. It is documented in Section 17.2.
• The Machine Learning PR (in the Machine Learning plugin) is GATE’s older
machine learning offering. It offers wrappers for Maxent, Weka and SVM Light. It is
documented in Section 17.3.
The rest of the chapter is organised as follows. Section 17.1 introduces machine learning in
general, focusing on the terminology used and the meaning of the terms within GATE. We
then move on to describe the two Machine Learning processing resources, beginning with the
Batch Learning PR in Section 17.2. Section 17.2.1 describes all the configuration settings of
the Batch Learning PR one by one; i.e. all the elements in the configuration file for setting
the Batch Learning PR (the learning algorithm to be used and the options for learning) and
defining the NLP features for the problem. Section 17.2.2 presents three case studies with
example configuration files for the three types of NLP learning problems. Section 17.2.3
lists the steps involved in using the Batch Learning PR. Finally, Section 17.2.4 explains the
outputs of the Batch Learning PR for the four usage modes; namely training, application,
evaluation and producing feature files only, and in particular, the format of the feature files
and label list file produced by the Batch Learning PR. Section 17.3 outlines the original
Machine Learning PR in GATE.
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17.1
ML Generalities
There are two main types of ML; supervised learning and unsupervised learning. Supervised
learning is more effective and much more widely used in NLP. Classification is a particular
example of supervised learning, in which the set of training examples is split into multiple
subsets (classes) and the algorithm attempts to distribute new examples into the existing
classes. This is the type of ML that is used in GATE, and all further references to ML
actually refer to classification.
An ML algorithm ‘learns’ about a phenomenon by looking at a set of occurrences of that
phenomenon that are used as examples. Based on these, a model is built that can be used
to predict characteristics of future (unseen) examples of the phenomenon.
An ML implementation has two modes of functioning: training and application. The training
phase consists of building a model (e.g. a statistical model, a decision tree, a rule set, etc.)
from a dataset of already classified instances. During application, the model built during
training is used to classify new instances.
Machine Learning in NLP falls broadly into three categories of task type; text classification,
chunk recognition, and relation extraction
• Text classification classifies text into pre-defined categories. The process can be
equally well applied at the document, sentence or token level. Typical examples of
text classification might be document classification, opinionated sentence recognition,
POS tagging of tokens and word sense disambiguation.
• Chunk recognition often consists of two steps. First, it identifies the chunks of
interest in the text. It then assigns a label or labels to these chunks. However some
problems comprise simply the first step; identifying the relevant chunks. Examples of
chunk recognition include named entity recognition (and more generally, information
extraction), NP chunking and Chinese word segmentation.
• Relation extraction determines whether or not a pair of terms in the text has some
type(s) of pre-defined relations. Two examples are named entity relation extraction
and co-reference resolution.
Typically, the three types of NLP learning use different linguistic features and feature representations. For example, it has been recognised that for text classification the so-called
tf − idf representation of n-grams is very effective (e.g. with SVM). For chunk recognition,
identifying the start token and the end token of the chunk by using the linguistic features
of the token itself and the surrounding tokens is effective and efficient. Relation extraction
benefits from both the linguistic features from each of the two terms involved in the relation
and the features of the two terms combined.
The rest of this section explains some basic definitions in ML and their specification in the
ML plugin.
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17.1.1
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Some Definitions
• instance: an example of the studied phenomenon. An ML algorithm learns a model
from a set of known instances, called a (training) dataset. It can then apply the learned
model to another (application) dataset.
• attribute: a characteristic of the instances. Each instance is defined by the values
of its attributes. The set of possible attributes is well defined and is the same for
all instances in the training and application datasets. ‘Feature’ is also often used.
However, in this context, this can cause confusion with GATE annotation features.
• class: an attribute for which the values are available in the training dataset for learning,
but which are not present in the application dataset. ML is used to find the value of
this attribute in the application dataset.
17.1.2
GATE-Specific Interpretation of the Above Definitions
• instance: an annotation. In order to use ML in GATE, users will need to choose the
type of annotations used as instances. Token annotations are a good candidate for
many NLP learning tasks such as information extraction and POS tagging, but any
type of annotation could be used (e.g. things that were found by a previously run
JAPE grammar, such as sentence annotations and document annotations for sentence
and document classification respectively).
• attribute: an attribute is the value of a named feature of a particular annotation
type, which can either (partially) cover the instance annotation considered or another
instance annotation which is related to the instance annotation considered. The value
of the attribute can refer to the current instance or to an instance either situated at
a specified location relative to the current instance or having special relation with the
current instance.
• class: any attribute referring to the current instance can be marked as class attribute.
17.2
Batch Learning PR
This section describes the newest machine learning PR in GATE. The implementation focuses on the three main types of learning in NLP, namely chunk recognition (e.g. named
entity recognition), text classification and relation extraction. The implementation for chunk
recognition is based on our work using support vector machines (SVM) for information extraction [Li et al. 05a]. The text classification is based on our work on opinionated sentence
classification and patent document classification (see [Li et al. 07c] and [Li et al. 07d], respectively). The relation extraction is based on our work on named entity relation extraction
[Wang et al. 06].
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The Batch Learning PR, given a set of documents, can also produce feature files, containing
linguistic features and feature vectors, and labels if there are any in the documents. It can
also produce document-term matrices and n-gram based language models. Feature files are
in text format and can be used outside of GATE. Hence, users can use GATE-produced
feature files off-line, for their own purpose, e.g. evaluating new learning algorithms.
The PR also provides facilities for active learning, based on support vector machines (SVM),
mainly ranking the unlabelled documents according to the confidence scores of the current
SVM models for those documents.
The primary learning algorithm implemented is SVM, which has achieved state of the art
performances for many NLP learning tasks. The training of SVM uses a Java version of
the SVM package LibSVM [CC001]. Application of SVM is implemented by ourselves. The
PAUM (Perceptron Algorithm with Uneven Margins) is also included [Li et al. 02], and
on our test datasets has consistently produced a performance to rival the SVM with much
reduced training times. Moreover, the ML implementation provides an interface to the opensource machine learning package Weka [Witten & Frank 99], and can use machine learning
algorithms implemented in Weka. Three widely-used learning algorithms are available in the
current implementation: Naive Bayes, KNN and the C4.5 decision tree algorithm.
Access to ML implementations is provided in GATE by the ‘Batch Learning PR’ (in the
‘learning’ plugin). The PR handles training and application of an ML model, evaluation
of learning on GATE documents, producing feature files and ranking documents for Active
Learning. It also makes it possible to view the primal forms of a linear SVM. This PR is a
Language Analyser so it can be used in all default types of GATE controllers.
In order to use the Batch Learning processing resource, the user has to do three things.
First, the user has to annotate some training documents with the labels that s/he wants
the learning system to annotate in new documents. Those label annotations should be
GATE annotations. Secondly, the user may need to pre-process the documents to obtain
linguistic features for the learning. Again, these features should be in the form of GATE
annotations. GATE’s plugin ANNIE might be helpful for producing the linguistic features.
Other resources such as the NP Chunker and parser may also be helpful. By providing the
machine learning algorithm with more and better information on which to base learning,
chances of a good result are increased, so this preprocessing stage is important. Finally the
user has to create a configuration file for setting the ML PR, e.g. selecting the learning
algorithm and defining the linguistic features used in learning. Three example configuration
files are presented in this section; it might be helpful to take one of them as a starting point
and modify it.
17.2.1
Batch Learning PR Configuration File Settings
In order to allow for more flexibility, all configuration parameters for the PR are set through
one external XML file, except for the learning mode, which is selected through normal PR
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parameterisation. The XML file contains both the configuration parameters of the Batch
Learning PR itself and of the linguistic data (namely the definitions of the instance and
attributes) used by the Batch Learning PR. The XML file is specified when creating a new
Batch Learning PR.
The parent directory of the XML configuration file becomes the working directory. A subdirectory in the working directory, named ‘savedFiles’, will be created (if it does not already
exist). All the files produced by the Batch Learning PR, including the NLP features files,
label list file, feature vector file and learned model file, will be stored in that subdirectory.
A log file recording the learning session is also created in this directory.
Below, we first describe the parameters of the Batch Learning PR. Then we explain those
settings specified in the configuration file.
PR Parameters: Settings not Specified in the Configuration File
For the sake of convenience, a few settings are not specified in the configuration file. Instead
the user should specify them as initialization or run-time parameters of the PR, as in other
PRs.
• URL (or path and name) of the configuration file. The user is required to give
the URL of the configuration file when creating the PR. The configuration file should
be in XML format with the extension name .xml. It contains most of learning settings
and will be explained in detail in the next subsection.
• Corpus. This is a run-time parameter, meaning that the user should specify it after
creating the PR, and may change it between runs. The corpus contains the documents
that the PR will use as learning data (training or application). For application, the
documents should include all the annotations specified in the configuration file, except
the class attribute. The annotations for class attribute should be available in the
documents used for training or evaluation.
• inputASName is the annotation set containing the annotations for the linguistic
features to be used and the class labels.
• outputASName is the annotation set in which the results of applying the models
will be put. Note that it should be set the same as the inputASName when doing the
evaluation (i.e. setting the learningMode as ‘EVALUATION’).
• learningMode is a run-time parameter. It can be set as one of the following
values, ‘TRAINING’, ‘APPLICATION’, ‘EVALUATION’, ‘ProduceFeatureFilesOnly’,
‘MITRAINING’, ‘VIEWPRIMALFORMMODELS’ and ‘RankingDocsForAL’. The default learning mode is ‘TRAINING’.
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– In TRAINING mode, the PR learns from the data provided and saves the
models into a file called ‘learnedModels.save’ under the sub-directory ‘savedFiles’
of the working directory.
– If the user wants to apply the learned model to the data, s/he should select
APPLICATION mode. In application mode, the PR reads the learned model
from the file ‘learnedModels.save’ in the subdirectory ‘savedFiles’ and then applies
the model to the data.
– In EVALUATION mode, the PR will do k-fold or hold-out test set evaluation on
the corpus provided (the method of the evaluation is specified in the configuration
file, see below), and output the evaluation results to the messages window of
GATE Developer, or standard out when using GATE Embedded, and into the
log file. When using evaluation mode, please make sure that the outputASName
is set to the same annotation set as the inputASName.
– If the user only wants to produce feature data and feature vectors but does not
want to train or apply a model, s/he may select the ProduceFeatureFilesOnly
mode. The feature files that the PR produces will be explained in detail in Section
17.2.4.
– In MITRAINING (mixed initiative training) mode, the training data are appended to the end of any existing feature file. In contrast, in training mode,
the training data created in the current session overwrite any existing feature file.
Consequently, mixed initiative training mode uses both the training data obtained
in this session and the data that existed in the feature file before starting the session. Hence, training mode is for batch learning, while mixed initiative training
mode can be used for on-line (or adaptive, or mixed-initiative) learning. There is
one parameter for mixed initiative training mode specifying the minimal number
of newly added documents before starting the learning procedure to update the
learned model. The parameter can be defined in the configuration file.
– VIEWPRIMALFORMMODELS mode is used for displaying the most salient
NLP features in the learned models. In the current implementation, the mode is
only valid with the linear SVM model, in which the most salient NLP features
correspond to the biggest (absolute values of) weights in the weight vector. In
the configuration file one can specify two parameters to determine the number
of displayed NLP features for positive and negative weights. Note that if e.g.
the number for negative weight is set as 0, then no NLP feature is displayed for
negative weights.
– RankingDocsForAL applies the current learned SVM models (in the subdirectory ‘savedFiles’) to the feature vectors stored in the file ‘fvsDataSelecting.save’ in the sub-directory ‘savedFiles’ and ranks the documents according to
the margins of the examples in one document to the SVM models. The ranked
list of documents will be put into the file ‘ALRankedDocs.save’.
In most cases it is not safe to run more than one instance of the batch learning PR with the
same working directory at the same time, because the PR needs to update the model (in
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TRAINING, MITRAINING or EVALUATION mode) or other data files. It is safe to run
multiple instances at once provided they are all in APPLICATION mode1 .
Order of document processing In the usual case, in a GATE corpus pipeline application, documents are processed one at a time, and each PR is applied in turn to the document,
processing it fully, before moving on to the next document. The Batch Learning PR breaks
from this rule. ML training algorithms, including SVM, typically run as a batch process
over a training set, and require all the data to be fully prepared and passed to the algorithm
in one go. This means that in training (or evaluation) mode, the Batch Learning PR will
wait for all the documents to be processed and will then run as a single operation at the
end. Therefore, the Batch Learning PR needs to be positioned last in the pipeline. Postprocessing cannot be done within the pipeline after the Batch Learning PR. Where further
processing needs to be done, this should take the form of a separate application, and be
applied to the data afterwards.
There is an exception to the above, however. In application mode, the situation is slightly
different, since the ML model has already been created, and the PR only applies it to the
data. This can be done on a document by document basis, in the manner of a normal PR.
However, although it can be done document by document, there may be advantages in terms
of efficiency to grouping documents into batches before applying the algorithm. A parameter
in the configuration file, BATCH-APP-INTERVAL, described later, allows the user to specify
the size of such batches, and by default this is set to 1; in other words, by default, the Batch
Learning PR in application mode behaves like a normal PR and processes each document
separately. There may be substantial efficiency gains to be had through increasing this
parameter (although higher values require more memory consumption), but if the Batch
Learning PR is applied in application mode and the parameter BATCH-APP-INTERVAL
is set to 1, the PR can be treated like any other, and other PRs may be positioned after it
in a pipeline.
Settings in the Batch Learning PR XML Configuration File
The root element of the XML configuration file needs to be called ‘ML-CONFIG’, and it
must contain two basic elements; DATASET and ENGINE, and optionally other settings. In
the following, we first describe the optional settings, then the ENGINE element, and finally
the DATASET element. In the next section, some examples of the XML configuration file
are given for illustration. Please also refer to the configuration files in the test directory (i.e.
plugs/learning/test/ under the main gate directory) for more examples.
1
This is only true for GATE 5.2 or later; in earlier versions all modes were unsafe for multiple instances
of the PR.
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Optional Settings in the Configuration File The Batch Learning PR provides a variety of optional settings, which facilitate different tasks. Every optional setting has a default
value; if an optional setting is not specified in the configuration file, the Batch Learning PR
will adopt its default value. Each of the following optional settings can be set as an element
in the XML configuration file.
• SURROUND should be set to ‘true’ if the user wants the Batch Learning PR to learn
chunks by identifying the start token and the end token of the chunk. This approach to
chunk learning, for example, named entity recognition, where a span of several tokens
is to be identified, often produces better results than trying to learn every token in the
chunk. For classification problems and relation extraction, set its value as ‘false’. This
element appears in the configuration file as:
<SURROUND VALUE=’X’/>
where the variable X has two possible values: ‘true’ or ‘false’. The default value is
‘false’.
• FILTERING relates to SVM training. Where the ratio of positive examples to negative examples is low, i.e. the instances belonging in the class are much outweighed
by instances outside of the class (e.g. ‘one against others’ is used, see multiClassification2Binary below) SVMs can run into difficulties. The positive examples may
be swamped by outlying negative examples. The ML plugin provides functionality
developed through research (e.g. [Li & Bontcheva 08]) to assist in such cases. One example is the FILTERING parameter. The filtering functionality performs initial SVM
training, then removes negative examples on the basis of their position relative to the
separator. It then retrains on the smaller dataset. Typically, negative instances close
to the boundary are removed. Note that this two-step process takes longer than simple
training. However, the second training step will be quicker than the first, as it is performed on a somewhat reduced dataset. If the item dis is set as ‘near’, the PR selects
and removes those negative examples which are closest to the SVM hyper-plane. If it
is set as ‘far’, those negative examples that are furthest from the SVM hyper-plane are
removed. The value of the item ratio determines what proportion of negative examples
will be filtered out. This element appears in the configuration file as:
< FILTERING ratio=’X’ dis=’Y’/>
where X represents a number between 0 and 1 and Y can be set as ‘near’ or ‘far’. If the
filtering element is not present in the configuration file, or the value of ratio is set as
0.0, the PR does not perform filtering. The default value of ratio is 0.0. The default
value of dis is ‘far’.
• EVALUATION As outlined above, if the learning mode parameter learningMode is
set to ‘EVALUATION’, the PR will perform evaluation of the ML model; it will split
the documents in the corpus into two parts, the training dataset and the test dataset,
learn a model from the training dataset, apply the model to the testing dataset, and
finally compare the annotations assigned by the model on the test set with the true
annotations and output measures of success (e.g. F-measure). The evaluation element
specifies the method of splitting the corpus. The item method determines which method
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to use for evaluation. Currently two commonly used methods are implemented, namely
k-fold cross-validation and hold-out test. In k-fold cross-validation the PR segments
the corpus into k partitions of equal size, and uses each of the partitions in turn as
a test set, with all the remaining documents as a training set. For hold-out test, the
system randomly selects some documents as testing data and uses all other documents
as training data. The value of the item runs specifies the number ‘k’ for k-fold crossvalidation. The value of the item ratio specifies the ratio of the data used for training
in the hold-out test method. The element in the configuration file appears as so:
<EVALUATION method=”X” runs=”Y” ratio=”Z”/>
where the variable X has two possible values ‘kfold’ and ‘holdout’, Y is a positive integer,
and Z is a float number between 0 and 1. The default value of method is ‘holdout’.
The default value of runs is ‘1’. The default value of ratio is ‘0.66’.
• multiClassification2Binary. Certain machine learning algorithms, including SVM,
are designed to operate on two class problems; they find a separator between two groups
of instances. In order to use such algorithms to classify items into a larger number of
classes, the problem has to be converted into a series of ‘binary’ (two class) problems.
The ML plugin implements two common methods for converting a multi-class problem
into several binary problems, namely one against others and one against another. The
two methods may have slightly different names in other publications, but the principle
is the same. Suppose we have a multi-class classification problem with n classes. For
the one against others method, one binary classification problem is derived for each of
the n classes. Examples belonging to the class in question are considered to be positive
examples and all other examples in the training set are negative examples. In contrast,
for the one against another method, one binary classification problem is derived for
each pair (c1, c2) of the n classes. Training examples belonging to the class c1 are the
positive examples and those belonging to the other class, c2, are the negative examples.
The user can select one of the two methods by specifying the value of the item method
of the element. The element appears as so:
<multiClassification2Binary method=”X” thread-pool-size=”N”/>
where the variable X has two values, ‘one-vs-others’ and ‘one-vs-another’. Note that
depending on the sample size, the two methods may differ greatly in their speed of
execution. The default method is the one-vs-others method. If the configuration file
does not have the element or the item method is missed, then the PR will use the onevs-others method. Since the derived binary classifiers are independent it is possible to
learn several of them in parallel. The ‘thread-pool-size’ attribute gives the number of
threads that will be used to learn and apply the binary classifiers. If omitted, a single
thread will be used to process all the classifiers in sequence.
• thresholdProbabilityBoundary sets a confidence threshold on start and end tokens
for chunk learning. It is used in post-processing the learning results. Only those
boundary tokens in which the confidence level is above the threshold are selected as
candidates for the entities. The element in configuration file appears as so:
<PARAMETER name=”thresholdProbabilityBoundary” value=”X”/>
The value X is between 0 and 1. The default value is 0.4.
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• thresholdProbabilityEntity sets a confidence threshold on chunks (which is the
multiplication of the probabilities of the start token and end token of the chunk) for
chunk learning. Only those entities in which the confidence level is above the threshold
are selected as candidates of the entities. The element in configuration file appears as
so:
<PARAMETER name=”thresholdProbabilityEntity” value=”X”/>
The value X is between 0 and 1. The default value is 0.2.
• The threshold parameter thresholdProbabilityClassification is the confidence
threshold for classification (e.g. text classification and relation extraction tasks. In
contrast, the above two probabilities are for the chunking recognition task.) The corresponding element in configuration file appears as so:
<PARAMETER name=”thresholdProbabilityClassification” value=”X”/>
The value X is between 0 and 1. The default value is 0.5.
• IS-LABEL-UPDATABLE is a Boolean parameter. If its value is set to ‘true’, the
label list is updated from the labels in the training data. Otherwise, a pre-defined label
list will be used and cannot be updated from the training data. The configuration
element appears as so:
<IS-LABEL-UPDATABLE value=”X”/>
The value X is ‘true’ or ‘false’. The default value is ‘true’.
• IS-NLPFEATURELIST-UPDATABLE is a Boolean parameter. If its value is set
to ‘true’, the NLP feature list is updated from the features in the training or application
data. Otherwise, a pre-defined NLP feature list will be used and cannot be updated.
The configuration element appears as so:
<IS-NLPFEATURELIST-UPDATABLE value=”X”/>
The value X is ‘true’ or ‘false’. The default value is ‘true’.
• The parameter VERBOSITY specifies the verbosity level of the output of the system,
both to the message window of GATE Developer (or standard out when using GATE
Embedded) and into the log file. Currently there are three verbosity levels. Level 0
only allows the output of warning messages. Level 1 outputs some important setting
information and the results for evaluation mode. Level 2 is used for debugging purposes.
The element in the configuration file appears as so:
<VERBOSITY level=”X”/>
The value X can be set as 0, 1 or 2. The default value is 1.
• MI-TRAINING-INTERVAL specifies the minimal number of newly added documents needed to trigger retraining the model. This parameter is used in MITRAINING.
The number is specified by the value of the feature ‘num’ as so:
<MI-TRAINING-INTERVAL num=”X”/>
The default value of X is 1.
• BATCH-APP-INTERVAL is used in application mode, and specifies the number
of documents to be collected and passed as a batch for classification. Please refer to
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Section 17.2.1 for a detailed explanation of this option. The corresponding element in
the configuration file is:
<BATCH-APP-INTERVAL num=”X”/>
The default value of X is 1.
• DISPLAY-NLPFEATURES-LINEARSVM relates to ‘VIEWPRIMALFORMMODELS’ mode. In this mode, the most significant features are displayed for each
class. For more information about this mode see Section 17.2.1. Two numbers are
specified; the number of positively weighted features to display and the number of
negatively weighted features to display. It has the following form in the configuration
file;
<DISPLAY-NLPFEATURES-LINEARSVM numP=”X” numN=”Y”/>
where X and Y represent the numbers of positively and negatively weighted features to
display, respectively. The default values of X and Y are 10 and 0.
• ACTIVELEARNING specifies the settings for active learning. Active learning ranks
documents based on the average of a sample of ML annotation confidence scores. A
larger sample gives a more accurate ranking but takes longer to calculate. The option
has the following form:
<ACTIVELEARNING numExamplesPerDoc=’X’/>
where X represents the number of examples per document used to obtain the confidence
score with respect to the learned model. The default value of numExamplesPerDoc is
3.
The ENGINE Element The ENGINE element specifies which ML algorithm will be
used, and also allows the options to be set for that algorithm.
For SVM learning, the user can choose one of two learning engines. We will discuss the two
SVM learning engines below. Note that only linear and polynomial kernels are supported.
This is despite the fact that the original SVM packages implemented other types of kernel.
Linear and polynomial kernels are popular in natural language learning, and other types of
kernel are rarely used. However, if you want to experiment with other types of kernel, you
can do so by first running the Batch Learning PR in GATE to produce the training and
testing data, then using the data with the SVM implementation outside of GATE.
The configuration files in the test directory (i.e. plugins/learning/test/ under the main gate
directory) contain examples for setting the learning engine.
The ENGINE element in the configuration file is specified as follows:
<ENGINE nickname=’X’ implementationName=’Y’ options=’Z’/>
It has three items:
• nickname can be the name of the learning algorithm or whatever the user wants it to
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be.
• implementationName refers to the implementation of the particular learning algorithm that the user wants to use. Its value should be one of the following:
– SVMLibSvmJava, the binary classification SVM algorithm implemented in the
Java version of the SVM package LibSVM.
– SVMExec, a binary SVM implementation of your choice, potentially in a language other than Java, run as a separate process outside of GATE. Currently
it can use the SV M light SVM package2 ; see the XML file in the GATE distribution (at gate/plugins/learning/test/chunklearning/engines-svm-svmlight.xml) for
an example of how to specify the learning engine to be used. The learning engines SVMExec and SVMLibSvmJava should produce the same results in theory
but may get slightly different results in practice due to implementational differences. SVMLibSvmJava tends to be faster than SVMExec for smaller training
sets. There may be cases where it is an advantage to run SVM as a separate
process however, in which case, SVMExec would be preferable.
– PAUM, the Perceptron with uneven margins, a simple and fast classification learning algorithm. (For details about the learning algorithm PAUM, see
[Li et al. 02]).
– PAUMExec, a binary PAUM implementation of your choice, potentially in a
language other than Java, run as a separate process outside of GATE. The relationship between the PAUM and PAUMExec is similar to that of SVMLibSvmJava and SVMExec. You may download and use an implementation in C from
http://www.dcs.shef.ac.uk/∼yaoyong/paum/paum-learning.zip. See the XML file
in the GATE distribution (at gate/plugins/learning/test/chunklearning/enginespaum-exec.xml) for an example of how to specify the learning engine to be used.
– NaiveBayesWeka, the Naive Bayes learning algorithm implemented in Weka.
– KNNWeka, the K nearest neighbour (KNN) algorithm implemented in Weka.
– C4.5Weka, the decision tree algorithm C4.5 implemented in Weka.
• Options: the value of this item, which is dependent on the particular learning algorithm, will be passed verbatim to the ML engine used. Where an option is absent,
defaults for that engine will be used.
– The options for SVMLibSvmJava are similar to those for LibSVM but with the
exception that since SVMLibSvmJava implements the uneven margins SVM algorithms described in [Li & Shawe-Taylor 03], it takes the uneven margins parameter as an option. SVMLibSvmJava options are as follows:
∗ -s svm type; whether the SVM should be binary or multiclass. Default
value is 0. Since only binary is supported, the option should be set to 0 or
excluded.
2
The SVM package SV M light can be downloaded from http://svmlight.joachims.org/.
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∗ -t kernel type; 0 for a linear kernel or 1 for a polynomial kernel. Default
value is 0. Note that the current implementation does not support other
kernel types such as radial and sigmoid function.
∗ -d degree; the degree in polynomial kernel, e.g. 2 for quadratic kernel.
Default value is 3.
∗ -c cost; the cost parameter C in the SVM. Default value is 1. This parameter
determines the cost associated with allowing training errors (‘soft margins’).
Allowing some points to be misclassified by the SVM may produce a more
generalizable result.
∗ -m cachesize; the cache memory size in MB (default 100).
∗ -tau value; setting the value of uneven margins parameter of the SVM.
τ = 1 corresponds to the standard SVM. If the training data has just a small
number of positive examples and a large number of negative examples, setting
the parameter τ to a value less than 1 (e.g. τ = 0.4) often results in better
F-measure than the standard SVM (see [Li & Shawe-Taylor 03]).
– The options for SVMExec, using SV M light , are similar to those for using SV M light
directly for training. Options set the type of kernel, the parameters in the kernel function, the cost parameter, the memory used, etc. The parameter tau is
also included, to set the uneven margins parameter, as explained above. The
last two terms in the parameter options are the training data file and the model
file. An example of the options for SVMExec might be ‘-c 0.7 -t 0 -m 100 -v
0 -tau 0.6 /yaoyong/software/svm-light/data svm.dat /yaoyong/software/svmlight/model svm.dat’, meaning that the learner uses a linear kernel, the uneven margins parameter is set as 0.6, and two data files /yaoyong/software/svmlight/data svm.dat and /yaoyong/software/svm-light/model svm.dat for writing
and reading data. Note that both the data files specified here are temporary
files, which are used only by the svm-light training program, can be in anywhere
in your computer, and are independent of the data files produced by the GATE
learning plugin. SVMExec also takes a further argument, executableTraining,
which specifies the SVM learning program svm learn.exe in the SV M light . For example, executableTraining=‘/yaoyong/software/svm-light/svm learn.exe’ specifies one particular svm learn.exe obtained from the package SV M light .
– The PAUM engine has three options; ‘-p’ for the positive margin, ‘-n’ fo the
negative margin, and ‘-optB’ for the modification of the bias term. For example,
options=‘-p 50 -n 5 -optB 0.3’ means τ+ = 50, τ− = 5 and b = b + 0.3 in the
PAUM algorithm.
– The KNN algorithm has one option; the number of neighbours used. It is set via
‘-k X’. The default value is 1.
– There are no options for Naive Bayes and C4.5 algorithms.
The DATASET Element The DATASET element defines the type of annotation to
be used as training instance and the set of attributes that characterise the instances. The
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INSTANCE-TYPE sub-element is used to select the annotation type to be used for instances.
There will be one training instance for every one of the instance annotations in the corpus.
For example, if INSTANCE-TYPE has ‘Token’ as its value, there will be one training instance
in the document per token. This also means that the positions (see below) are defined
in relation to tokens. INSTANCE-TYPE can be seen as the basic unit to be taken into
account for machine learning. The attributes of the instance are defined by a sequence of
ATTRIBUTE, ATTRIBUTE REL or ATTRIBUTELIST elements.
Different NLP learning tasks may have different instance types and use different kinds of
attribute elements. Chunking recognition often uses the token as instance type and the linguistic features of ‘Token’ and other annotations as features. Text classification’s instance
type is the text unit for classification, e.g. the whole document, or sentence, or token. If
classifying for example a sentence, n-grams (see below) are often a good feature representation for many statistical learning algorithms. For relation extraction, the instance type
is a pair of terms that may be related, and the features come from not only the linguistic
features of each of the two terms but also those related to both terms taken together.
The DATASET element should define an INSTANCE-TYPE sub-element, it should define
an ATTRIBUTE sub-element or an ATTRIBUTE REL sub-element as class, and it should
define some linguistic feature related sub-elements (‘linguistic feature’ or ‘NLP feature’ is
used here to distinguish features or attributes used for machine learning from features in the
sense of a feature of a GATE annotation). All the annotation types involved in the dataset
definition should be in the same annotation set. Each of the sub-elements defining the
linguistic features (attributes) should contain an element defining the annotation TYPE to
be used and an element defining the FEATURE of the annotation type to use. For instance,
TYPE might be ‘Person’ and FEATURE might be ‘gender’. For an ATTRIBUTE subelement, if you do not specify FEATURE, the entire sub-element will be ignored. Therefore,
if an annotation type you want to use does not have any annotation features, you should add
an annotation feature to it and assign the same value to the feature for all annotations of
that type. Note that if blank spaces are contained in the values of the annotation features,
they will be replaced by the character ‘ ’ in each occurrence. So it is advisable that the
values of the annotation features used, in particular for the class label, do not contain any
blank space.
Below, we explain all the sub-elements one by one. Please also refer to the example configuration files presented in next section. Note that each sub-element should have a unique
name, if it requires a name, unless we explicitly state otherwise.
• The INSTANCE-TYPE sub-element is defined as
<INSTANCE-TYPE>X</INSTANCE-TYPE> where X is the annotation type used
as instance unit for learning, for example ‘Token’. For relation extraction, the user
should also specify the two arguments of the relation, as so:
<INSTANCE-ARG1>A</INSTANCE-ARG1>
<INSTANCE-ARG2>B</INSTANCE-ARG2>
The values of A and B should be identifiers for the first and second terms of the relation,
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respectively. These names will be used later in the configuration file. An example can
be found at /gate/plugins/learning/test/relation-learning/engines-svm.xml.
• An ATTRIBUTE element has the following sub-elements:
– NAME; the name of the attribute. Its value should not end with ‘gram’, since
this is reserved for n-gram features as mentioned below. This attribute name will
appear in output files, so it is useful to give a descriptive name.
– SEMTYPE; type of the attribute value. It can be ‘NOMINAL’ or ‘NUMERIC’.
Currently only nominal is supported.
– TYPE; the annotation type used to extract the attribute.
– FEATURE; the value of the attribute will be the value of the named feature on
the annotation of the specified type.
– POSITION; the position of the instance annotation to be used for extracting
the feature relative to the current instance annotation. 0 refers to the current
instance annotation, -1 refers to the preceding instance annotation, 1 refers to
the following one and so forth. Recall that we defined INSTANCE-TYPE at the
start of the DATASET element. This type might for example be ‘Token’. In the
current ATTRIBUTE element we are defining an annotation type to use to get
the feature from, separate and possibly different from the INSTANCE-TYPE. For
example, we might be interested in the ‘majorType’ of a ‘Lookup’. By specifying
-1, we would be saying, move to the preceding ‘Token’ and then try to extract the
‘majorType’ of the ‘Lookup’ on that token. The default value of the parameter
is 0. Note that if our INSTANCE-TYPE were to be for example a named entity
annotation comprising multiple tokens, and we wanted to extract a feature on the
‘Token’ annotation, then all the tokens within it would be considered to be in the
zero position relative to the current instance annotation, and the current implementation would simply pick the first. (Useful in this case might be the NGRAM
attribute type, described later, which can be used to extract features for each
member of a multi-token annotation.) In the current implementation, features
are weighted according to their distance from the current instance annotation. In
other words, features which are further removed from the current instance annotation are given reduced importance. The component value in the feature vector
for one attribute feature is 1 if the attribute’s position p is 0. Otherwise its value
is 1.0/|p|.
– <CLASS/>: an empty element used to mark the class attribute. There can
only be one attribute marked as class in a dataset definition. The attribute, as
described above, has specified TYPE and FEATURE; the features of the type
are the class labels. Since only one attribute can be marked as class, it may be
necessary to preprocess your data to put all class labels into a feature of one type
of annotation, e.g. you might create a ‘Mention’ annotation, with the feature
‘Class’, which is set to the class name.
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• The ATTRIBUTELIST element is similar to ATTRIBUTE except that it has no POSITION sub-element but instead a RANGE element. This will be converted into several
attributes with position ranging from the value of ‘from’ to the value of ‘to’. It defines
a ‘context window’ containing several consecutive examples. The ATTRIBUTELIST
should be preferred when defining a context window for features, because not only it
can avoid the duplication of ATTRIBUTE elements, but also because processing is
speeded up (see the discussion for the element WINDOWSIZE below).
• The WINDOWSIZE element specifies the size of the context window. This will
override the context window size defined in every ATTRIBUTELIST. If the WINDOWSIZE element is not present in the configuration file, the window size defined in
each element ATTRIBUTELIST will be used; otherwise, the window size specified by
this element will be used for each ATTRIBUTELIST if it contains one ATTRIBUTE
at position 0 (otherwise the ATTRIBUTELIST will be ignored). This element can be
used for speeding up the process of extracting the feature vectors from the documents.
The element has two features specifying the length of left and right sides of context
window. It has the following form:
<WINDOWSIZE windowSizeLeft=”X” windowSizeRight=”Y”/>
where X and Y represent the the length of left and right sides of context window, respectively. For example, if X = 2 and Y = 1, then the context window will be from the
position -2 to 1 ( e.g. from the second token in the left through the current token to
the first token in the right).
• An NGRAM feature is used for characterising an instance annotation in terms of
constituent sequences of subsumed feature annotations. It is essentially a reversal of the
ATTRIBUTELIST principle; where ATTRIBUTELIST uses a sequence surrounding an
instance in order to classify the instance, NGRAM uses sequences within the instance as
features. It simply creates a series of attributes that constitute a sliding window across
the entire of the current instance annotation. For example, INSTANCE-TYPE might
be sentences, in sentence classification, and the NGRAM attribute specification could
be used for example to create a series of unigram features for the sentence, effectively
a ‘bag of words’ representation. Conventionally, one would use the string of the token,
or perhaps its lemma, as the feature for the NGRAM; however, it is possible to specify
multiple features of choice, as shown below.
– NAME; name of the n-gram. Its value should end with ‘gram’.
– NUMBER; the ‘n’ of the n-gram, with value 1 for unigram, and 2 for bigram,
etc.
– CONSNUM; several features can be used to generate n-grams. For example,
n-grams of token strings could be used as well as n-grams of lemmas. Where
CONSNUM is ‘k’, the NGRAM element should have ‘k’ CONS-X sub-elements,
where X= 1, ..., k. Each CONS-X element has one TYPE sub-element and one
FEATURE sub-element, which define feature to be used for that term to create
n-grams.
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– The WEIGHT sub-element specifies a weight for the n-gram feature. The ngram part of the feature vector for one instance is normalised, thus having a
default value of 1.0. If the user wants to adjust the contributions of the n-gram to
the whole feature vector, s/he can do so by setting the WEIGHT parameter. For
example, if the user is doing sentence classification and s/he uses two features;
the unigram of tokens in a sentence and the length of the sentence, by default the
entire of the NGRAM attribute specification is given only the same importance as
the sentence length feature. In order to experiment with increasing the importance
of the n-gram element, the user can set the weight sub-element of the n-gram
element with a number bigger than 1.0 (like 10.0). Then every component of the
n-gram part of the feature vector would be multiplied by the parameter.
• The ValueTypeNgram element specifies the type of value used in the n-gram. Currently it can take one of the three types; ‘binary, tf, and tf-idf, which are explained in
Section 17.2.4. The value is specified by the X in
<ValueTypeNgram>X</ValueTypeNgram>
X = 1 for binary, = 2 for tf, and = 3 for tf-idf. The default value is 3.
• The FEATURES-ARG1 element defines the features related to the first argument of
the relation for relation learning. It should include one ARG sub-element referring to
the GATE annotation of the argument (see below for a detailed explanation). It may include other sub-elements, such as ATTRIBUTE, ATTRIBUTELIST and/or NGRAM,
to define the linguistic features related to the argument. Features pertaining particularly to one or the other argument of a relation should be defined in FEATURES-ARG1
or FEATURES-ARG2 as appropriate. Features relating to both arguments should be
defined using an ATTRIBUTE REL.
• The FEATURES-ARG2 element defines the features related to the second argument of relation. Like the element FEATURES-ARG1, it should include one ARG
sub-element. It may also include other sub-elements. The ARG sub-element in the
FEATURES-ARG2 should have a unique name which is different from the name for
the ARG sub-element in the FEATURES-ARG1. However, other sub-elements may
have the same name as corresponding ones in the FEATURES-ARG1, if they refer to
the same annotation type and feature in the text.
• The ARG element is used in both FEATURES-ARG1 and FEATURES-ARG2. It
specifies the annotation corresponding to one argument of a relation. It has four subelements, as follows;
– NAME; a unique name for the argument (e.g. ‘ARG1’).
– SEMTYPE; the type of the arg value. This can be ‘NOMINAL’ or ‘NUMERIC’.
Currently only nominal is implemented.
– TYPE; the annotation type for the argument.
– FEATURE; the value of the named feature on the annotation of specified type is
the identifier of the argument. Only if the value of the feature is same as the value
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of the feature specified in the sub-element <INSTANCE-ARG1>A</INSTANCEARG1> (or <INSTANCE-ARG2>B</INSTANCE-ARG2>), the argument is regarded as one argument of the relation instance considered.
• ATTRIBUTE REL element is similar to the ATTRIBUTE element. However, it
does not have the POSITION sub-element, and it has two other sub-elements, ARG1
and ARG2, relating to the two argument features of the (relation) instance type. In
other words, if and only if the value X in the sub-element <ARG1>X</ARG1> is same
as the value A in the first argument instance <INSTANCE-ARG1>A</INSTANCEARG1> and the value Y in the sub-element <ARG2>Y</ARG2> is same as the value
B in the second argument instance <INSTANCE-ARG2>B</INSTANCE-ARG2> is
the feature defined in this ATTRIBUTE REL sub-element assigned to the instance
considered. For relation learning, an ATTRIBUTE REL is denoted as the class attribute by including <CLASS/>.
17.2.2
Case Studies for the Three Learning Types
The following are three illustrated examples of configuration files for information extraction,
sentence classification and relation extraction. Note that the configuration file is in the XML
format, and should be stored in a file with the ‘.xml’ extension.
Information Extraction
The first example is for information extraction. The corpus is prepared with annotations
providing class information as well as the features to be used. Class information is provided in
the form of a single annotation type, ‘Mention’, which contains a feature ‘class’. Within the
class feature is the name of the class of the textual chunk. Other annotations in the dataset
include ‘Token’ and ‘Lookup’ annotations as provided by ANNIE. All of these annotations
are in the same annotation set, the name of which will be passed as a runtime parameter.
The configuration file is given below. The optional settings are in the first part. It first
specifies surround mode as ‘true’; we will find the chunks that correspond to our entities
by using machine learning to locate the start and end of the chunks. Then it specifies the
filtering settings. Since we are going to use SVM in this problem, we can filter our data to
remove some of the negative instances that can cause problems if they are too dominant.
The ratio’s value is ‘0.1’ and the dis’s value is ‘near’, meaning that an initial SVM learning
step will be executed and the 10% of negative examples which are closest to the learned
SVM hyper-plane will be removed in the filtering stage, before the final learning is executed.
The threshold probabilities for the boundary tokens and information entity are set as ‘0.4’
and ‘0.2’, respectively; boundary tokens found with a lower confidence than the threshold
will be rejected. The threshold probability for classification is also set as ‘0.5’; this, however,
will not be used in this case since we are doing chunk learning with surround mode set as
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‘true’. The parameter will be ignored. multiClassification2Binary is set as ‘one-vs-others’,
meaning that the ML API will convert the multi-class classification problem into a series of
binary classification problems using the one against others approach. In evaluation mode,
‘2-fold’ cross-validation will be used, dividing the corpus into two equal parts and running
two training/test cycles with each part as the training data.
The second part is the sub-element ENGINE, specifying the learning algorithm. The PR
will use the LibSVM SVM implementation. The options determine that it will use the linear
kernel with the cost C as 0.7 and the cache memory as 100M. Additionally it will use uneven
margins, with τ as 0.4.
The last part is the DATASET sub-element, defining the linguistic features used. It first
specifies the ‘Token’ annotation as instance type. The first ATTRIBUTELIST allows the
token’s string as a feature of an instance. The range from ‘-5’ to ‘5’ means that the strings
of the current token instance as well as its five preceding tokens and its five ensuing tokens
will be used as features for the current token instance. The next two attribute lists define
features based on the tokens’ capitalisation information and types. The ATTRIBUTELIST
named ‘Gaz’ uses as attributes the values of the feature ‘majorType’ of the annotation type
‘Lookup’. The final ATTRIBUTE feature defines the class attribute; it has the sub-element
<CLASS/>. The values of the feature ‘class’ of the annotation type ‘Mention’ are the class
labels.
<?xml version="1.0"?>
<ML-CONFIG>
<SURROUND value="true"/>
<FILTERING ratio="0.1" dis="near"/>
<PARAMETER name="thresholdProbabilityEntity" value="0.2"/>
<PARAMETER name="thresholdProbabilityBoundary" value="0.4"/>
<PARAMETER name="thresholdProbabilityClassification" value="0.5"/>
<multiClassification2Binary method="one-vs-others"/>
<EVALUATION method="kfold" runs="2"/>
<ENGINE nickname="SVM" implementationName="SVMLibSvmJava"
options=" -c 0.7 -t 0 -m 100 -tau 0.4 "/>
<DATASET>
<INSTANCE-TYPE>Token</INSTANCE-TYPE>
<ATTRIBUTELIST>
<NAME>Form</NAME>
<SEMTYPE>NOMINAL</SEMTYPE>
<TYPE>Token</TYPE>
<FEATURE>string</FEATURE>
<RANGE from="-5" to="5"/>
</ATTRIBUTELIST>
<ATTRIBUTELIST>
<NAME>Orthography</NAME>
<SEMTYPE>NOMINAL</SEMTYPE>
<TYPE>Token</TYPE>
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<FEATURE>orth</FEATURE>
<RANGE from="-5" to="5"/>
</ATTRIBUTELIST>
<ATTRIBUTELIST>
<NAME>Tokenkind</NAME>
<SEMTYPE>NOMINAL</SEMTYPE>
<TYPE>Token</TYPE>
<FEATURE>kind</FEATURE>
<RANGE from="-5" to="5"/>
</ATTRIBUTELIST>
<ATTRIBUTELIST>
<NAME>Gaz</NAME>
<SEMTYPE>NOMINAL</SEMTYPE>
<TYPE>Lookup</TYPE>
<FEATURE>majorType</FEATURE>
<RANGE from="-5" to="5"/>
</ATTRIBUTELIST>
<ATTRIBUTE>
<NAME>Class</NAME>
<SEMTYPE>NOMINAL</SEMTYPE>
<TYPE>Mention</TYPE>
<FEATURE>class</FEATURE>
<POSITION>0</POSITION>
<CLASS/>
</ATTRIBUTE>
</DATASET>
</ML-CONFIG>
Sentence Classification
We will now consider the case of sentence classification. The corpus in this example is annotated with ‘Sentence’ annotations, which contain the feature ‘sent size’, as well as the class
of the sentence. Furthermore, ‘Token’ annotations are applied, having features ‘category’
and ‘root’. As before, all annotations are in the same set, and the annotation set name will
be passed to the PR at run time.
Below is an example configuration file. It first specifies surround mode as ‘false’, because it
is a text classification problem; we are interested in classifying single instances rather than
chunks of instances. Our targets of interest, sentences, have already been found (unlike in
the information extraction example, where identifying the limits of the entity was part of the
problem). The next two options allow the label list and the NLP feature list to be updated
from the training data when retraining. It also specifies probability thresholds for entity and
entity boundary. Note that these two specifications will not be used in this case. However,
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their presence is not problematic; they will simply be ignored. The probability threshold for
classification is set as ‘0.5’. This will be used to decide which classifications to accept and
which to reject as being too unlikely. (Altering this parameter can trade off precision against
recall and vice versa.) The evaluation will use the hold-out test method. It will randomly
select 66% of the documents from the corpus for training, and the other 34% documents
will be used for testing. It will run the evaluation twice, and average the results over the
two runs. Note that it does not specify the method of converting a multi-class classification
problem into several binary class problem, meaning that it will adopt the default (namely
one against all others).
The configuration file specifies KNN (K-Nearest Neighbour) as the learning algorithm. It
also specifies the number of neighbours used as 5. Of course other learning algorithms can
be used as well. For example, the ENGINE element in the previous example, which specifies
SVM as learning algorithm, can be put into this configuration file to replace the current one.
In the DATASET element, the annotation ‘Sentence’ is used as instance type. Two kinds of
linguistic features are defined; one is NGRAM and the other is ATTRIBUTE. The n-gram
is based on the annotation ‘Token’. It is a unigram, as its NUMBER element has the value
1. This means that a ‘bag of words’ feature will be formed from the tokens comprising the
sentence. It is based on the two features, ‘root’ and ‘category’, of the annotation ‘Token’.
This introduces a new aspect to the n-gram. The n-gram feature comprises counts of the
unigrams appearing in the sentence. For example, if the sentence were ‘the man walked
the dog”, the unigram feature would contain the information that ‘the’ appeared twice,
and ‘man’, ‘walked’ and ‘dog’ appeared once. However, since our n-gram has two features,
‘root’ and ‘category’, two tokens will be considered the same term if and only if they have
the same ‘root’ feature and the same ‘category’ feature. The weight of the ngram is set
as 10.0, meaning its contribution is ten times that of the contribution of the other feature,
the sentence length. The feature ‘sent size’ of the annotation ‘Sentence’ is given as an
ATTRIBUTE feature. Finally the values of the feature ‘class’ of the annotation ‘Sentence’
are nominated as the class labels.
<?xml version="1.0"?>
<ML-CONFIG>
<SURROUND value="false"/>
<IS-LABEL-UPDATABLE value="true"/>
<IS-NLPFEATURELIST-UPDATABLE value="true"/>
<PARAMETER name="thresholdProbabilityEntity" value="0.2"/>
<PARAMETER name="thresholdProbabilityBoundary" value="0.42"/>
<PARAMETER name="thresholdProbabilityClassification" value="0.5"/>
<EVALUATION method="holdout" runs="2" ratio="0.66"/>
<ENGINE nickname="KNN" implementationName="KNNWeka" options = " -k 5 "/>
<DATASET>
<INSTANCE-TYPE>Sentence</INSTANCE-TYPE>
<NGRAM>
<NAME>Sent1gram</NAME>
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<NUMBER>1</NUMBER>
<CONSNUM>2</CONSNUM>
<CONS-1>
<TYPE>Token</TYPE>
<FEATURE>root</FEATURE>
</CONS-1>
<CONS-2>
<TYPE>Token</TYPE>
<FEATURE>category</FEATURE>
</CONS-2>
<WEIGHT>10.0</WEIGHT>
</NGRAM>
<ATTRIBUTE>
<NAME>Class</NAME>
<SEMTYPE>NOMINAL</SEMTYPE>
<TYPE>Sentence</TYPE>
<FEATURE>sent_size</FEATURE>
<POSITION>0</POSITION>
</ATTRIBUTE>
<ATTRIBUTE>
<NAME>Class</NAME>
<SEMTYPE>NOMINAL</SEMTYPE>
<TYPE>Sentence</TYPE>
<FEATURE>class</FEATURE>
<POSITION>0</POSITION>
<CLASS/>
</ATTRIBUTE>
</DATASET>
</ML-CONFIG>
Relation Extraction
The last example is for relation extraction. The relation extraction support in the PR is
based on the work described in [Wang et al. 06].
Two concepts are key in a relation extraction corpus. Entities are the things that may be
related, and relations describe the relationship between the entities if any. In our example,
entities are pre-identified, and the task is to identify the relationships between them. The
corpus for this example is annotated with the following:
• ‘ACEEntity’ annotations indicate the entities of interest in the corpus.
• ‘RE INS’ annotations form the instances, and there is an instance for every pair of
‘ACEEntities’ within a sentence. ‘RE INS’ annotations span the entire of the text
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between and including their ‘ACEEntity’ annotations. For example, ‘the commander
of Israeli troops’ might be a potential relationship between a person, ‘the commander’, and an entity, ‘Israeli troops’. Its ‘RE INS’ annotation covers the entire of this
text. It contains ‘arg1’ and ‘arg2’ features containing the numerical identifiers of the
two ‘ACEEntities’ to which it pertains. These numerical identifiers match the ‘MENTION ID’ feature of the ‘ACEEntity’ annotation.
• ‘ACERelation’ annotations indicate the relations we wish to learn, and also span
the entire of the text involved in the relationship. They include the features ‘MENTION ARG1’ and ‘MENTION ARG2’, which, again, contain the numerical identifier
found in the ‘MENTION ID’ feature of the ‘ACEEntity’ annotations, as well as ‘Relation type’, indicating the type of the relation.
• Various ANNIE-style annotations are also included.
Our task is to select the ‘RE INS’ instances that match the ‘ACERelations’. You will see
that throughout the configuration file, annotation types are specified in conjunction with
argument identifiers. This is because we need to ensure that the annotation in question
pertains to the right entities. Therefore, argument identifiers are used to constrain the
match.
The configuration file does not specify any optional settings, meaning that it uses all the
default values for those settings (see Section 17.2.1 for the default values of all possible
settings).
• it sets the surround mode as ‘false’;
• both the label list and NLP feature list are updatable;
• the probability threshold for classification is set as 0.5;
• it uses ‘one against others’ for converting multi-class problem into binary class problems
for SVM learning;
• for evaluation it uses hold-out testing with a ratio of 0.66 and only one run.
The configuration file specifies the learning algorithm as the Naive Bayes method implemented in Weka. However, other learning algorithms could equally well be used.
We begin by defining ‘RE INS’ as the instance type. Next, we provide the numeric identifiers of each argument of the relationship by specifying elements INSTANCE-ARG1 and
INSTANCE-ARG2 as the feature names ‘arg1’ and ‘arg2’ respectively. This indicates that
the argument identifiers of the instances can be found in the ‘arg1’ and ‘arg2’ features of the
‘RE INS’ annotations.
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Attributes might pertain to the entire relation or they might pertain to one or other argument
within the relation. We are going to begin by defining the features specific to each argument
of the relation. Recall that our ‘RE INS’ annotations have as arguments two ‘ACEEntity’ annotations, and that these are identified by their ‘MENTION ID’ being the same as the ‘arg1’
or ‘arg2’ features of the ‘RE INS’. It is from these ‘ACEEntity’ annotations that we wish
to obtain argument-specific features. FEATURES-ARG1 and FEATURES-ARG1 elements
begin by specifying which annotation we are referring to. We use the ARG element to explain this. We are interested in annotations of type ‘ACEEntity’, and their ‘MENTION ID’
must match ‘arg1’ or ‘arg2’ of ‘RE INS’ as appropriate. Having identified precisely which
‘ACEEntity’ we are interested in we can go on to give argument-specific features; in this
case, unigrams of the ‘Token’ feature ‘string’.
We now wish to define features pertaining to the entire relation. We indicate that the ‘t12’
feature of ‘RE INS’ annotations is to be used (this feature contains type information derived
from ‘ACEEntity’). Again, rather than just specifying the ‘RE INS’ annotation, we also
indicate that the ‘arg1’ and ‘arg2’ feature values must match the argument identifiers of
the instance, as defined in the INSTANCE-ARG1 and INSTANCE-ARG2 elements at the
beginning. This ensures that we are taking our features from the correct annotation.
Finally, we define the class attribute. We indicate that the class attribute is contained in
the ‘Relation type’ feature of the ‘ACERelation’ annotation. The ‘ACERelation’ annotation
type has features ‘MENTION ARG1’ and ‘MENTION ARG1’, indicating its arguments.
Again, we use the elements ARG1 and ARG2 to indicate that it is these features that must
be matched to the arguments of the instance if that instance is to be considered a positive
example of the class.
<?xml version="1.0"?>
<ML-CONFIG>
<ENGINE nickname="NB" implementationName="NaiveBayesWeka"/>
<DATASET>
<INSTANCE-TYPE>RE_INS</INSTANCE-TYPE>
<INSTANCE-ARG1>arg1</INSTANCE-ARG1>
<INSTANCE-ARG2>arg2</INSTANCE-ARG2>
<FEATURES-ARG1>
<ARG>
<NAME>ARG1</NAME>
<SEMTYPE>NOMINAL</SEMTYPE>
<TYPE>ACEEntity</TYPE>
<FEATURE>MENTION_ID</FEATURE>
</ARG>
<ATTRIBUTE>
<NAME>Form</NAME>
<SEMTYPE>NOMINAL</SEMTYPE>
<TYPE>Token</TYPE>
<FEATURE>string</FEATURE>
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<POSITION>0</POSITION>
</ATTRIBUTE>
</FEATURES-ARG1>
<FEATURES-ARG2>
<ARG>
<NAME>ARG2</NAME>
<SEMTYPE>NOMINAL</SEMTYPE>
<TYPE>ACEEntity</TYPE>
<FEATURE>MENTION_ID</FEATURE>
</ARG>
<ATTRIBUTE>
<NAME>Form</NAME>
<SEMTYPE>NOMINAL</SEMTYPE>
<TYPE>Token</TYPE>
<FEATURE>string</FEATURE>
<POSITION>0</POSITION>
</ATTRIBUTE>
</FEATURES-ARG2>
<ATTRIBUTE_REL>
<NAME>EntityCom1</NAME>
<SEMTYPE>NOMINAL</SEMTYPE>
<TYPE>RE_INS</TYPE>
<ARG1>arg1</ARG1>
<ARG2>arg2</ARG2>
<FEATURE>t12</FEATURE>
</ATTRIBUTE_REL>
<ATTRIBUTE_REL>
<NAME>Class</NAME>
<SEMTYPE>NOMINAL</SEMTYPE>
<TYPE>ACERelation</TYPE>
<ARG1>MENTION_ARG1</ARG1>
<ARG2>MENTION_ARG2</ARG2>
<FEATURE>Relation_type</FEATURE>
<CLASS/>
</ATTRIBUTE_REL>
</DATASET>
</ML-CONFIG>
17.2.3
How to Use the Batch Learning PR in GATE Developer
The Batch Learning PR implements the procedure of using supervised machine learning
for NLP, which generally has two steps; training and application. The training step learns
models from labelled data. The application step applies the learned models to the unlabelled
data in order to add labels. Therefore, in order to use supervised ML for NLP, one should
have some labelled data, which can be obtained either by manually annotating documents
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or from other resources. One also needs to determine which linguistic features are to be
used in training. (The same features should be used in the application as well.) In this
implementation, all machine learning attributes are GATE annotation features. Finally, one
should determine which learning algorithm will be used.
Based on the general procedure outlined above, we explain how to use the Batch Learning
PR step by step below:
1. Annotate some documents with labels that you want to learn. The labels should be
represented by the values of a feature of a GATE annotation type (not the annotation
type itself).
2. Determine the linguistic features that you want the PR to use for learning.
3. Annotate the documents (training and application) with the desired features. ANNIE
can be useful in this regard. Other PRs such as GATE morphological analyser and
the parsers may produce useful features as well. You may need to write some JAPE
scripts to produce the features you want.
4. Create an XML configuration file for your learning problem. The file should contain one
DATASET element specifying the NLP features used, one ENGINE element specifying
the learning algorithm, and some optional settings as necessary. (Tip: it may be easier
to copy one of the configuration files presented above and modify it for your problem
than to write a configuration file from scratch.)
5. Load the training documents containing the required annotations representing the
linguistic features and the class label, and put them into a corpus. All linguistic
features and the class feature should be in the same annotation set. (The Annotation
Set Transfer PR in the ‘Tools’ plugin can be useful here.)
6. Load the Batch Learning PR into GATE Developer. First you need load the plugin
named ‘learning’ using the tool Manage CREOLE Plugins. Then you can create a new
‘Batch Learning PR’. You will need to provide the configuration file as an initialization
parameter. After that you can put the PR into a Corpus Pipeline application to use
it. Add the corpus containing the training documents to the application too. Set the
inputASName to the annotation set containing the annotations for linguistic features
and class labels.
7. Set the run-time parameter learningMode to ‘TRAINING’ to learn a model from the
training data, or set learningMode to ‘EVALUATION’ to do evaluation on the training
data and get figures indicating the success of the learning. When using evaluation
mode, make sure that the outputASName is the same as the inputASName. (Tip: it
may save time if you first try evaluation mode on a small number of documents to
make sure that the ML PR works well on your problem and outputs reasonable results
before training on the large data.)
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8. If you want to apply the learned model to new documents, load those new documents
into GATE and pre-process them in the same way as the training documents, to ensure
that the same features are present. (Class labels need not be present, of course.) Then
set learningMode to ‘APPLICATION’ and run the PR on this corpus. The application
results, namely the new annotations containing the class labels, will be added into the
annotation set specified by the outputASName.
9. If you just want the feature files produced by the system and do not want to do any
learning or application, select the learning mode ‘ProduceFeatureFilesOnly’.
17.2.4
Output of the Batch Learning PR
The Batch Learning PR outputs several different kinds of information. Firstly, it outputs
information about the learning settings. This information will be printed in the Messages
Window of the GATE Developer (or standard out if using GATE Embedded) and also into
the log file ‘logFileForNLPLearning.save’. The amount of information displayed can be determined via the VERBOSITY parameter in the configuration file. The main output of the
learning system is different for different usage modes. In training mode the system produces the learned models. In application mode it annotates the documents using the learned
models. In evaluation mode it displays the evaluation results. Finally, in ‘ProduceFeatureFilesOnly’ mode, it produces feature files for the current corpus. Below, we explain the
outputs for different learning modes.
Note that all the files produced by the Batch Learning PR, including the log file, are placed
in the sub-directory ‘savedFiles’ of the ML working directory. The ML working directory is
the directory containing the configuration file.
Training results
When the Batch Learning PR is used in training mode, its main output is the learned model,
stored in a file named ‘learnedModels.save’. For the SVM algorithm, the learned model file
is a text file. For the learning algorithms implemented in Weka, the model file is a binary
file. The output also includes the feature files described in Section 17.2.4.
Application Results
The main application result is the annotations added to the documents. Those annotations
are the results of applying the ML model to the documents. In the configuration file, the
annotation type and feature of the class labels are specified; class labels must be the value of
a feature of an annotation type. In application mode, those annotation types are created in
the new documents, and the feature specified will hold the class label. An additional feature
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will also be included on the specified annotation type; ‘prob’ will hold the confidence level
for the annotation.
Evaluation Results
The Batch Learning PR outputs the evaluation results for each run and also the averaged
results over all runs. For each run, it first prints a message about the names of the documents
in training and testing corpora respectively. Then it displays the evaluation results of this
run; first the results for each class label and then the micro-averaged results over all labels.
For each label, it presents the name of the label, the number of instances belonging to the
label in the training data and results on the test data; the numbers of correct, partially
correct, spurious and missing instances in the testing data, and the precision, recall and
F1, calculated using correct only (strict) and correct plus partial (lenient). The F-measure
results are obtained using the AnnotationDiff Tool which is described in Chapter 10. Finally,
the system presents the means of the results of all runs for each label and the micro-averaged
results.
Feature Files
The Batch Learning PR is able to produce several feature files. These feature files could
be used for evaluating learning algorithms not implemented in this plugin. We describe the
formats of those feature files below. Note that all the data files described below can be
obtained by setting the run time parameter learningMode to ‘ProduceFeatureFilesOnly’, but
some may be produced as part of other learning modes.
The NLP feature file, named NLPFeatureData.save, contains the NLP features of the
instances defined in the configuration file. Below is an example of the first few lines of an
NLP feature file for information extraction:
Class(es) Form(-1) Form(0) Form(1) Ortho(-1) Ortho(0) Ortho(1)
0 ft-airlines-27-jul-2001.xml 512
1 Number_BB _NA[-1] _Form_Seven _Form_UK[1] _NA[-1] _Ortho_upperInitial
_Ortho_allCaps[1]
1 Country_BB _Form_Seven[-1] _Form_UK _Form_airlines[1] _Ortho_upperInitial[-1]
_Ortho_allCaps _Ortho_lowercase[1]
0 _Form_UK[-1] _Form_airlines _Form_including[1] _Ortho_allCaps[-1]
_Ortho_lowercase _Ortho_lowercase[1]
0 _Form_airlines[-1] _Form_including _Form_British[1] _Ortho_lowercase[-1]
_Ortho_lowercase _Ortho_upperInitial[1]
1 Airline_BB _Form_including[-1] _Form_British _Form_Airways[1]
_Ortho_lowercase[-1] _Ortho_upperInitial _Ortho_upperInitial[1]
1 Airline _Form_British[-1] _Form_Airways _Form_[1], _Ortho_upperInitial[-1]
_Ortho_upperInitial _NA[1]
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0 _Form_Airways[-1] _Form_, _Form_Virgin[1] _Ortho_upperInitial[-1] _NA
_Ortho_upperInitial[1]
The first line of the NLP feature file lists the names of all features used. These names are the
names the user gave to their features in the configuration file. The number in the parenthesis
following a feature name indicates the position of the feature. For example, ‘Form(-1)’ means
the Form feature of the token which is immediately before the current token, and ‘Form(0)’
means the Form feature of the current token. The NLP features for all instances are listed
for one document before moving on to the next. For each document, the first line shows the
index of the document, the document’s name and the number of instances in the document,
as shown in the second line above. After that, each line corresponds to an instance in the
document, in their order of appearance. The first item on the line is a number n, representing
the number of class labels of the instance. Then, the following n items are the labels. If the
current instance is the first instance of an entity, its corresponding label has a suffix ‘ BB’.
The other items following the label item(s) are the NLP features of the instance, in the order
listed in the first line of the file. Each NLP feature contains the feature’s name and value,
separated by ‘ ’. At the end of one NLP feature, there may be an integer in square brackets,
which represents the position of the feature relative to the current instance. If there is no
square-bracketed integer at the end of one NLP feature, then the feature is at the position
0.
The Feature vector file has the file name ‘featureVectorsData.save’, and stores the feature
vector in sparse format for each instance. The first few lines of the feature vector file
corresponding to the NLP feature file shown above are as follows:
0
1
2
3
4
5
6
7
512 ft-airlines-27-jul-2001.xml
2 1 2 439:1.0 761:1.0 100300:1.0 100763:1.0
2 3 4 300:1.0 763:1.0 50439:1.0 50761:1.0 100440:1.0 100762:1.0
0 440:1.0 762:1.0 50300:1.0 50763:1.0 100441:1.0 100762:1.0
0 441:1.0 762:1.0 50440:1.0 50762:1.0 100020:1.0 100761:1.0
1 5 20:1.0 761:1.0 50441:1.0 50762:1.0 100442:1.0 100761:1.0
1 6 442:1.0 761:1.0 50020:1.0 50761:1.0 100066:1.0
0 66:1.0 50442:1.0 50761:1.0 100443:1.0 100761:1.0
The feature vectors are also listed for each document in sequence. For each document, the
first line shows the index of the document, the number of instances in the document and the
document’s name. Each of the following lines is for each of the instances in the document.
The first item in the line is the index of the instance in the document. The second item is
a number n, representing the number of labels the instance has. The following n items are
indices representing the class labels.
For text classification and relation learning, the label’s index comes directly from the label
list file, described below. For chunk learning, the label’s index presented in the feature vector
file is a bit more complicated. If an instance (e.g. token) is the first one of a chunk with
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label k, then the instance has as the label’s index 2 ∗ k − 1, as shown in the fifth instance.
If it is the last instance of the chunk, it has the label’s index as 2 ∗ k, as shown in the sixth
instance. If the instance is both the first one and the last one of the chunk (namely the
chunk consists of one instance), it has two label indices, 2 ∗ k − 1 and 2 ∗ k, as shown in the
first and second instances.
The items following the label(s) are the non-zero components of the feature vector. Each
component is represented by two numbers separated by ‘:’. The first number is the dimension
(position) of the component in the feature vector, and the second one is the value of the
component.
The Label list file has the name ‘LabelsList.save’, and stores a list of labels and their
indices. The following is a part of a label list. Each line shows one label name and its index
in the label list.
Airline 3
Bank 13
CalendarMonth 11
CalendarYear 10
Company 6
Continent 8
Country 2
CountryCapital 15
Date 21
DayOfWeek 4
The NLP feature list has the name ‘NLPFeaturesList.save’, and contains a list of NLP
features and their indices in the list. The following are the first few lines of an NLP feature
list file.
totalNumDocs=14915
_EntityType_Date 13 1731
_EntityType_Location 170 1081
_EntityType_Money 523 3774
_EntityType_Organization 12 2387
_EntityType_Person 191 421
_EntityType_Unknown 76 218
_Form_’ 112 775
_Form_\$ 527 74
_Form_’ 508 37
_Form_’s 63 731
_Form_( 526 111
The first line of the file shows the number of instances from which the NLP features were
collected. The number of instances will be used for computating of the idf (inverse document
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frequency) in document or sentence classification. The following lines are for the NLP
features. Each line is for one unique feature. The first item in the line represents the NLP
feature, which is a combination of the feature’s name defined in the configuration file and
the value of the feature. The second item is a positive integer representing the index of the
feature in the list. The last item is the number of times that the feature occurs, which is
needed for computing the idf.
The N-grams (or language model) file has the name ‘NgramList.save’, and can only be
produced by setting the learning mode to ‘ProduceFeatureFilesOnly’. In order to produce
n-gram data, the user may use a very simple configuration file, i.e. it need only contain the
DATASET element, and the data element need contain only an NGRAM element to specify
the type of n-gram and the INSTANCE-TYPE element to define the annotation type from
which the n-gram data are created (e.g. sentence). The NGRAM element in configuration
file specifies what type of n-grams the PR produces (see Section 17.2.1 for the explanation
of the n-gram definition). For example, if you specify a bigram based on the string form of
‘Token’, you will obtain a list of bigrams from the corpus you used. The following are the
first lines of a bigram list based on the token annotation’s ‘string’ feature, and was calculated
over 3 documents.
## The following 2-gram were obtained from 3 documents or examples
Aug<>, 3
Female<>; 3
Human<>; 3
2004<>Aug 3
;<>Female 3
.<>The 3
of<>a 3
)<>: 3
,<>and 3
to<>be 3
;<>Human 3
The two terms of the bigram are separated by ‘<>’. The number following one n-gram is the
number of occurrences of that n-gram in the corpus. The n-gram list is ordered according
to the number of occurrences of the n-gram terms. The most frequent terms in the corpus
are therefore at the start of the list.
The n-gram data produced can be based on any features of annotations available in the
documents. Hence it can not only produce the conventional n-gram data based on the
token’s form or lemma, but also n-grams based on e.g. the token’s POS, or a combination
of the token’s POS and form, or any feature of the ‘sentence’ annotation (see Section 17.2.1
for how to define different types of n-gram).
The Document-term matrix file has the name ‘documentByTermMatrix.save’, and can
only be produced by setting the learning mode to ‘ProduceFeatureFilesOnly’. The documentterm matrix presents the weights of terms appearing in each document (see Section 20.14 for
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more explanation). Currently three types of weight are implemented; binary, term frequency
(tf) and tf-idf. The binary weight is simply 1 if the term appears in document and 0 if it does
not. tf (term frequency) refers to the number of occurrences of one term in a document. tf-idf
is popular in information retrieval and text mining. It is a multiplication of term frequency
and inverse document frequency. Inverse document frequency is calculated as follows:
idfi = log
|D|
|{dj : ti ∈ dj }|
where |D| is the total number of documents in the corpus, and |{dj : ti ∈ dj }| is the number
of documents in which the term ti appears. The type of weight is specified by the sub-element
ValueTypeNgram in the DATASET element in configuration file (see Section 17.2.1).
Like the n-gram data, in order to produce the document-term matrix, the user may use
a very simple configuration file, i.e. it need only contain the DATASET element, and the
data element need only contain two elements; the INSTANCE-TYPE element, to define
the annotation type from which the terms are counted, and an NGRAM element to specify
the type of n-gram. As mentioned previously, the element ValueTypeNgram specifies the
type of value used in the matrix. If it is not present, the default type tf-idf will be used.
The conventional document-term matrix can be produced using a unigram based on the
token’s form or lemma and the instance type covering the whole document. In other words,
INSTANCE-TYPE is set to an annotation type such as for example ‘body’, which covers the
entire document, and the n-gram definition then specifies the ‘string’ feature of the ‘Token’
annotation type.
The following was extracted from the beginning of a document-term matrix file, produced
using unigrams of the token’s form. It presents a part of the matrix of terms and their term
frequency values in the document named ‘27.xml’. Each term and its term frequency are
separated by ‘:’. The terms are in alphabetic order.
0 Documentname="27.xml", has 1 parts: ":2 (:6 ):6 ,:14 -:1 .:16 /:1
124:1 2004:1 22:1 29:1 330:1 54:1 8:2 ::5 ;:11 Abstract:1 Adaptation:1
Adult:1 Atopic:2 Attachment:3 Aug:1 Bindungssicherheit:1 Cross-:1
Dermatitis:2 English:1 F-SOZU:1 Female:1 Human:1 In:1 Index:1
Insecure:1 Interpersonal:1 Irrespective:1 It:1 K-:1 Lebensqualitat:1
Life:1 Male:1 NSI:2 Neurodermitis:2 OT:1 Original:1 Patients:1
Psychological:1 Psychologie:1 Psychosomatik:1 Psychotherapie:1
Quality:1 Questionnaire:1 RSQ:1 Relations:1 Relationship:1 SCORAD:1
Scales:1 Sectional:1 Securely:1 Severity:2 Skindex-:1 Social:1
Studies:1 Suffering:1 Support:1 The:1 Title:1 We:3 [:1 ]:1 a:4
absence:1 affection:1 along:2 amount:1 an:1 and:9 as:1 assessed:1
association:2 atopic:5 attached:7
A list of names of documents processed can also be obtained. The file has the name
‘docsName.save’, and only can be produced by setting the learning mode to ‘ProduceFea-
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tureFilesOnly’. It contains the names of all the documents processed. The first line shows
the number of documents in the list. Then, each line lists one document’s name. The first
lines of an example file are shown below:
##totalDocs=3
ft-bank-of-england-02-aug-2001.xml
ft-airtours-08-aug-2001.xml
ft-airlines-27-jul-2001.xml
A list of names of the selected documents for active learning purposes can also
be produced. The file has the name ‘ALSelectedDocs.save’. It is a text file. It is produced
in ‘ProduceFeatureFilesOnly’ mode. The file contains the names of documents which have
been selected for annotating and training in the active learning process. It is used by the
‘RankingDocsForAL’ learning mode to exclude those selected documents from the ranked
documents for active learning purposes. When one or more documents are selected for
annotating and training, their names should be put into this file, one line per document.
A list of names of ranked documents for active learning purposes; the file has
the name ‘ALRankedDocs.save’, and is produced in ‘RankingDocsForAL’ mode. The file
contains the list of names of the documents ranked for active learning, according to their
usefulness for learning. Those in the front of the list are the most useful documents for
learning. The first line in the file shows the total number of documents in the list. Each of
other lines in the file lists one document and the averaged confidence score for classifying
the document. An example of the file is shown below:
##numDocsRanked=3
ft-airlines-27-jul-2001.xml_000201 8.61744
ft-bank-of-england-02-aug-2001.xml_000221 8.672693
ft-airtours-08-aug-2001.xml_000211 9.82562
17.2.5
Using the Batch Learning PR from the API
Using the Batch Learning PR from the API is a simple matter if you have some familiarity
with GATE Embedded. Chapter 7 provides a more comprehensive introduction to programming with GATE Embedded, and should be consulted for any general points. There is also
a complete example program on the code examples page.
The following snippet shows creating a pipeline application, with a corpus, then creating a
batch learning PR and adding it to the application. The location of the configuration file
and the mode in which the PR is to be run are added to the PR. The application is then run.
‘corpus’ is a GATE corpus that you have previously set up. (To learn more about creating
a corpus from GATE Embedded, see chapter 7 or the example at the code examples page.)
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File configFile = new File ( " / home / you / ml_config . xml " ); //Wherever it is
RunMode mode = RunMode . EVALUATION ; //or TRAINING, or APPLICATION ..
3
4
5
6
7
8
9
10
//Make a pipeline and add the corpus
FeatureMap pfm = Factory . newFeatureMap ();
pfm . put ( " corpus " , corpus );
gate . creole . S e r i a l A n a l y s e r C o n t r o l l e r pipeline =
( gate . creole . S e r i a l A n a l y s e r C o n t r o l l e r )
gate . Factory . createResource (
" gate . creole . S e r i a l A n a l y s e r C o n t r o l l e r " , pfm );
11
12
13
14
15
16
17
18
19
20
21
//Set up the PR and add it to the pipeline.
//As with using the PR from GATE Developer, it needs a config file
//and a mode.
FeatureMap fm = Factory . newFeatureMap ();
fm . put ( " configFileURL " , configFile . toURI (). toURL ());
fm . put ( " learningMode " , mode );
gate . learning . LearningAPIMain learner =
( gate . learning . LearningAPIMain )
gate . Factory . createResource ( " gate . learning . LearningAPIMain " , fm );
pipeline . add ( learner );
22
23
24
//Run it!
pipeline . execute ();
Having run the PR in EVALUATION mode, you can access the results programmatically:
1
2
3
4
5
6
7
8
E v a l u a t io n B a s e d O n D o c s ev = learner . getEvaluation ();
System . out . println (
ev . m a c r o M e a s u r e s O f R e s u l t s . precision + " ," +
ev . m a c r o M e a s u r e s O f R e s u l t s . recall + " ," +
ev . m a c r o M e a s u r e s O f R e s u l t s . f1 + " ," +
ev . m a c r o M e a s u r e s O f R e s u l t s . precisionLenient + " ," +
ev . m a c r o M e a s u r e s O f R e s u l t s . recallLenient + " ," +
ev . m a c r o M e a s u r e s O f R e s u l t s . f1Lenient + " \ n " );
17.3
Machine Learning PR
The ‘Machine Learning PR’ is GATE’s earlier machine learning PR. It handles both the
training and application of ML model on GATE documents. This PR is a Language Analyser so it can be used in all default types of GATE controllers. It can be found in the
‘Machine Learning’ plugin.
In order to allow for more flexibility, all the configuration parameters for the Machine Learning PR are set through an external XML file and not through the normal PR parameterisation. The root element of the file needs to be called ‘ML-CONFIG’ and it contains two
elements: ‘DATASET’ and ‘ENGINE’. An example XML configuration file is given in Section
17.3.6.
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17.3.1
393
The DATASET Element
The DATASET element defines the type of annotation to be used as instance and the set of
attributes that characterise all the instances.
An ‘INSTANCE-TYPE’ element is used to select the annotation type to be used for instances,
and the attributes are defined by a sequence of ‘ATTRIBUTE’ elements.
For example, if an ‘INSTANCE-TYPE’ has a ‘Token’ for value, there will one instance in
the dataset per ‘Token’. This also means that the positions (see below) are defined in
relation to Tokens. The ‘INSTANCE-TYPE’ can be seen as the smallest unit to be taken
into account for the Machine Learning.
An ATTRIBUTE element has the following sub-elements:
• NAME: the name of the attribute
• TYPE: the annotation type used to extract the attribute.
• FEATURE (optional): if present, the value of the attribute will be the value of the
named feature on the annotation of specified type.
• POSITION: the position of the annotation used to extract the feature relative to the
current instance annotation.
• VALUES(optional): includes a list of VALUE elements.
• <CLASS/>: an empty element used to mark the class attribute. There can only be
one attribute marked as class in a dataset definition.
The VALUES being defined as XML entities, the characters <, > and & must be replaced
by &lt;, &rt; and &amp;. It is recommended to write the XML configuration file in UTF-8
in order that uncommon characters are correctly parsed.
Semantically, there are three types of attributes:
• nominal attributes: both type and features are defined and a list of allowed values
is provided;
• numeric: both type and features are defined but no list of allowed values is provided;
it is assumed that the feature can be converted to a number (a double value).
• boolean: no feature or list of values is provided; the attribute will take one of the
‘true’ or ‘false’ values based on the presence (or absence) of the specified annotation
type at the required position.
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Figure 17.1: Sample attributes and their values
Figure 17.1 gives some examples of what the values of specified attributes would be in a
situation when ‘Token’ annotations are used as instances.
An ATTRIBUTELIST element is similar to ATTRIBUTE except that it has no POSITION
sub-element but a RANGE element. This will be converted into several ATTRIBUTELIST
with position ranging from the value of the attribute ‘from’ to the value of the attribute ‘to’.
This can be used in order to avoid the duplication of ATTRIBUTE elements.
17.3.2
The ENGINE Element
The ENGINE element defines which particular ML implementation will be used, and allows
the setting of options for that particular implementation.
The ENGINE element has three sub-elements:
• WRAPPER: defines the class name for the ML implementation (or implementation
wrapper). The specified class needs to extend gate.creole.ml.MLEngine.
• BATCH-MODE-CLASSIFICATION: this element is optional. If present (as
an empty element <BATCH-MODE-CLASSIFICATION />), the training instances will be
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passed to the engine in a single batch. If absent, the instances are passed to the engine
one at a time. Not every engine supports this option, but for those that do, it can
greatly improve performance.
• OPTIONS: the contents of the OPTIONS element will be passed verbatim to the ML
engine used.
17.3.3
The WEKA Wrapper
The PR provides a wrapper for the WEKA ML Library (http://www.cs.waikato.ac.nz/ml/weka/)
in the form of the gate.creole.ml.weka.Wrapper class.
Options for the WEKA Wrapper
The WEKA wrapper accepts the following options:
• CLASSIFIER: the class name for the classifier to be used.
• CLASSIFIER-OPTIONS: the options string as required for the classifier.
• CONFIDENCE-THRESHOLD: a double value. If the classifier can provide a
probability distribution rather than a simple classification then all possible classifications that have a probability value larger or equal to the confidence threshold will be
considered.
• DATASET-FILE: location of the weka arff file. This item is not mandatory, it is
possible to specify the file using the saving option on the GUI.
Training an ML Model with the WEKA Wrapper
The Machine Learning PR has a Boolean runtime parameter named ”training”. When the
value of this parameter is set to true, the PR will collect a dataset of instances from the
documents on which it is run. If the classifier used is an updatable classifier then the ML
model will be built while collecting the dataset. If the selected classifier is not updatable,
then the model will be built the first time a classification is attempted.
Training a model consists of designing a definition file for the ML PR, and creating an
application containing a Machine Learning PR. When the application is run over a corpus,
the dataset (and the model if possible) is built.
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Applying a Learnt Model
Using the same PR, set the ‘training’ parameter to false and run your application.
Depending on the type of the attribute that is marked as class, different actions will be
performed when a classification occurs:
• if the attribute is boolean, a new annotation of the specified type will be created with
no features;
• if the attribute is nominal or numeric, a new annotation of the specified type will be
created with the feature named in the attribute definition having the value predicted
by the classifier.
Once a model is learnt, it can be saved and reloaded at a later time. The WEKA wrapper
also provides an operation for saving only the dataset in the ARFF format, which can be
used for experiments in the WEKA interface. This could be useful for determining the best
algorithm to be used and the optimal options for the selected algorithm.
17.3.4
The MAXENT Wrapper
GATE also provides a wrapper for the Open NLP MAXENT library
(http://maxent.sourceforge.net/about.html). The MAXENT library provides an implementation of the maximum entropy learning algorithm, and can be accessed using the
gate.creole.ml.maxent.MaxentWrapper class.
The MAXENT library requires all attributes except for the class attribute to be boolean, and
that the class attribute be boolean or nominal. (It should be noted that, within maximum
entropy terminology, the class attribute is called the ‘outcome’.) Because the MAXENT
library does not provide a specific format for data sets, there is no facility to save or load
data sets separately from the model, but if there should be a need to do this, the WEKA
wrapper can be used to collect the data.
Training a MAXENT model follows the same general procedure as for WEKA models, but
the following difference should be noted. MAXENT models are not updateable, so the model
will always be created and trained the first time a classification is attempted. The training
of the model might take a considerable amount of time, depending on the amount of training
data and the parameters of the model.
Options for the MAXENT Wrapper
• CUT-OFF: MAXENT features will only be included in the model if they occur at
least this many times. (The default value of this parameter is zero.)
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• ITERATIONS: The number of times the training procedure should iterate when
finding the model’s parameters (default is 10). In general no more than about 100
iterations should be needed to train a model, and it is recommended that less are used
during development to allow for shorter training times.
• CONFIDENCE-THRESHOLD: Same as for the WEKA wrapper (see above).
However, if this parameter is not set, or is set to zero, the model will not use a
confidence threshold, but will simply return the most likely classification.
• SMOOTHING: Use smoothing when training the model. Smoothing can improve the
accuracy of the learned models, but it will result in longer training times, and training
will use more memory. The size of the learned models will also be larger. Generally
smoothing will only improve performance for those models trained from small data
sets with a few outcomes. With larger data sets with lots of outcomes, it may make
performance worse.
• SMOOTHING-OBSERVATION: When using smoothing, this will specify the
number of times that trainer will imagine that it has seen features which it did not see
(default value is 0.1).
• VERBOSE: If selected, this will cause the classifier to output more details of its
operation during execution.
17.3.5
The SVM Light Wrapper
The PR provides a wrapper for the SVM Light ML system (http://svmlight.joachims.org).
SVM Light is a support vector machine implementation, written in C, which is provided as
a set of command line programs. The wrapper takes care of the mundane work of converting
the data structures between GATE and SVM Light formats, and calls the command line
programs in the right sequence, passing the data back and forth in temporary files. The
<WRAPPER> value for this engine is gate.creole.ml.svmlight.SVMLightWrapper.
The SVM Light binaries themselves are not distributed with GATE – you should download
the version for your platform from http://svmlight.joachims.org and place svm learn and
svm classify on your path.
Classifying documents using the SVMLightWrapper is a two phase procedure. In its first
phase, SVMWrapper collects data from the pre-annotated documents and builds the SVM
model using the collected data to classify the unseen documents in its second phase. Below
we describe briefly an example of classifying the start time of the seminar in a corpus of
email announcing seminars and provide more details later in the section.
Figure 17.2 explains step by step the process of collecting training data for the SVM classifier.
GATE documents, which are pre-annotated with the annotations of type Class and feature
type=’stime’, are used as the training data. In order to build the SVM model, we require start
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and end annotations for each stime annotation. We use a pre-processor JAPE transduction
script to mark the sTimeStart and sTimeEnd annotations on stime annotations. Following
this step, the Machine Learning PR (SVMLightWrapper) with training mode set to true
collects the training data from all training documents. A GATE corpus pipeline, given a
set of documents and PRs to execute on them, executes all PRs one by one, only on one
document at a time. Unless provided in a separate pipeline, it makes it impossible to send
all training data (i.e. collected from all documents) altogether to the SVMWrapper using
the same pipeline to build the SVM model. This results in the model not being built at the
time of collecting training data. The state of the SVMWrapper can be saved to an external
file once the training data is collected.
Figure 17.2: Flow diagram explaining the SVM training data collection
Before classifying any unseen document, SVM requires the SVM model to be available. In
the absence of an up-to-date SVM model, SVMWrapper builds a new one using a command
line SVM learn utility and the training data collected from the training corpus. In other
words, the first SVM model is built when a user tries to classify the first document. At this
point the user has an option to save the model somewhere. This is to enable reloading of
the model prior to classifying other documents and to avoid rebuilding of the SVM model
everytime the user classifies a new set of documents. Once the model becomes available,
SVMWrapper classifies the unseen documents which creates new sTimeStart and sTimeEnd
annotations over the text. Finally, a post-processor JAPE transduction script is used to
combine them into the sTime annotation. Figure 17.3 explains this process.
The wrapper allows support vector machines to be created which do either boolean classification or regression (estimation of numeric parameters), and so the class attribute can be
boolean or numeric. Additionally, when learning a classifier, SVM Light supports transduction, whereby additional examples can be presented during training which do not have the
value of the class attribute marked. Presenting such examples can, in some circumstances,
greatly improve the performance of the classifier. To make use of this, the class attribute
can be a three value nominal, in which case the first value specified for that nominal in the
configuration file will be interpreted as true, the second as false and the third as unknown.
Transduction will be used with any instances for which this attribute is set to the unknown
value. It is also possible to use a two value nominal as the class attribute, in which case it
will simply be interpreted as true or false.
The other attributes can be boolean, numeric or nominal, or any combination of these. If
an attribute is nominal, each value of that attribute maps to a separate SVM Light feature.
Each of these SVM Light features will be given the value 1 when the nominal attribute has
the corresponding value, and will be omitted otherwise. If the value of the nominal is not
specified in the configuration file or there is no value for an instance, then no feature will be
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Figure 17.3: Flow diagram explaining document classifying process
added.
An extension to the basic functionality of SVM Light is that each attribute can receive a
weighting. These weighting can be specified in the configuration file by adding <WEIGHTING>
tags to the parts of the XML file specifying each attribute. The weighting for the attribute
must be specified as a numeric value, and be placed between an opening <WEIGHTING> tag
and a closing </WEIGHTING> one. Giving an attribute a greater weighting, will cause it to
play a greater role in learning the model and classifying data. This is achieved by multiplying
the value of the attribute by the weighting before creating the training or test data that is
passed to SVM Light. Any attribute left without an explicitly specified weighting is given a
default weighting of one. Support for these weightings is contained in the Machine Learning
PR itself, and so is available to other wrappers, though at time of writing only the SVM
Light wrapper makes use of weightings.
As with the MAXENT wrapper, SVM Light models are not updateable, so the model
will be trained at the first classification attempt. The SVM Light wrapper supports
<BATCH-MODE-CLASSIFICATION />, which should be used unless you have a very good reason
not to.
The SVM Light wrapper allows both data sets and models to be loaded and saved to files
in the same formats as those used by SVM Light when it is run from the command line.
When a model is saved, a file will be created which contains information about the state
of the SVM Light Wrapper, and which is needed to restore it when the model is loaded
again. This file does not, however, contain any information about the SVM Light model
itself. If an SVM Light model exists at the time of saving, and that model is up to date with
respect to the current state of the training data, then it will be saved as a separate file, with
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the same name as the file containing information about the state of the wrapper, but with
.NativePart appended to the filename. These files are in the standard SVM Light model
format, and can be used with SVM Light when it is run from the command line. When a
model is reloaded by GATE, both of these files must be available, and in the same directory,
otherwise an error will result. However, if an up to date trained model does not exist at
the time the model is saved, then only one file will be created upon saving, and only that
file is required when the model is reloaded. So long as at least one training instance exists,
it is possible to bring the model up to date at any point simply by classifying one or more
instances (i.e. running the model with the training parameter set to false).
Options for the SVM Light Engine
Only one <OPTIONS> subelement is currently supported:
• <CLASSIFIER-OPTIONS> a string of options to be passed to svm learn on the command
line. The only difference is that the user should not specify whether regression or
classification is to be used, as the wrapper will detect this automatically, based on the
type of the class attribute, and set the option accordingly.
17.3.6
Example Configuration File
<?xml version="1.0" encoding="UTF-8"?>
<ML-CONFIG>
<DATASET>
<!-- The type of annotation used as instance -->
<INSTANCE-TYPE>Token</INSTANCE-TYPE>
<ATTRIBUTE>
<!-- The name given to the attribute -->
<NAME>Lookup(0)</NAME>
<!-- The type of annotation used as attribute -->
<TYPE>Lookup</TYPE>
<!-- The position relative to the instance annotation -->
<POSITION>0</POSITION>
</ATTRIBUTE>
<ATTRIBUTE>
<!-- The name given to the attribute -->
<NAME>Lookup_MT(-1)</NAME>
<!-- The type of annotation used as attribute -->
<TYPE>Lookup</TYPE>
<!-- Optional: the feature name for the feature used to extract values
for the attribute -->
<FEATURE>majorType</FEATURE>
<!-- The position relative to the instance annotation -->
<POSITION>-1</POSITION>
<!-- The list of permitted values.
if present, marks a nominal attribute;
if absent, the attribute is numeric (double)
-->
<VALUES>
<!-- One permitted value -->
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<VALUE>address</VALUE>
<VALUE>cdg</VALUE>
<VALUE>country_adj</VALUE>
<VALUE>currency_unit</VALUE>
<VALUE>date</VALUE>
<VALUE>date_key</VALUE>
<VALUE>date_unit</VALUE>
<VALUE>facility</VALUE>
<VALUE>facility_key</VALUE>
<VALUE>facility_key_ext</VALUE>
<VALUE>govern_key</VALUE>
<VALUE>greeting</VALUE>
<VALUE>ident_key</VALUE>
<VALUE>jobtitle</VALUE>
<VALUE>loc_general_key</VALUE>
<VALUE>loc_key</VALUE>
<VALUE>location</VALUE>
<VALUE>number</VALUE>
<VALUE>org_base</VALUE>
<VALUE>org_ending</VALUE>
<VALUE>org_key</VALUE>
<VALUE>org_pre</VALUE>
<VALUE>organization</VALUE>
<VALUE>organization_noun</VALUE>
<VALUE>percent</VALUE>
<VALUE>person_ending</VALUE>
<VALUE>person_first</VALUE>
<VALUE>person_full</VALUE>
<VALUE>phone_prefix</VALUE>
<VALUE>sport</VALUE>
<VALUE>spur</VALUE>
<VALUE>spur_ident</VALUE>
<VALUE>stop</VALUE>
<VALUE>surname</VALUE>
<VALUE>time</VALUE>
<VALUE>time_modifier</VALUE>
<VALUE>time_unit</VALUE>
<VALUE>title</VALUE>
<VALUE>year</VALUE>
</VALUES>
<!-- Optional: if present marks the attribute used as CLASS
Only one attribute can be marked as class -->
</ATTRIBUTE>
<ATTRIBUTE>
<!-- The name given to the attribute -->
<NAME>Lookup_MT(0)</NAME>
<!-- The type of annotation used as attribute -->
<TYPE>Lookup</TYPE>
<!-- Optional: the feature name for the feature used to extract values
for the attribute -->
<FEATURE>majorType</FEATURE>
<!-- The position relative to the instance annotation -->
<POSITION>0</POSITION>
<!-- The list of permitted values.
if present, marks a nominal attribute;
if absent, the attribute is numeric (double)
-->
<VALUES>
<!-- One permitted value -->
<VALUE>address</VALUE>
<VALUE>cdg</VALUE>
<VALUE>country_adj</VALUE>
<VALUE>currency_unit</VALUE>
<VALUE>date</VALUE>
<VALUE>date_key</VALUE>
<VALUE>date_unit</VALUE>
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<VALUE>facility</VALUE>
<VALUE>facility_key</VALUE>
<VALUE>facility_key_ext</VALUE>
<VALUE>govern_key</VALUE>
<VALUE>greeting</VALUE>
<VALUE>ident_key</VALUE>
<VALUE>jobtitle</VALUE>
<VALUE>loc_general_key</VALUE>
<VALUE>loc_key</VALUE>
<VALUE>location</VALUE>
<VALUE>number</VALUE>
<VALUE>org_base</VALUE>
<VALUE>org_ending</VALUE>
<VALUE>org_key</VALUE>
<VALUE>org_pre</VALUE>
<VALUE>organization</VALUE>
<VALUE>organization_noun</VALUE>
<VALUE>percent</VALUE>
<VALUE>person_ending</VALUE>
<VALUE>person_first</VALUE>
<VALUE>person_full</VALUE>
<VALUE>phone_prefix</VALUE>
<VALUE>sport</VALUE>
<VALUE>spur</VALUE>
<VALUE>spur_ident</VALUE>
<VALUE>stop</VALUE>
<VALUE>surname</VALUE>
<VALUE>time</VALUE>
<VALUE>time_modifier</VALUE>
<VALUE>time_unit</VALUE>
<VALUE>title</VALUE>
<VALUE>year</VALUE>
</VALUES>
<!-- Optional: if present marks the attribute used as CLASS
Only one attribute can be marked as class -->
</ATTRIBUTE>
<ATTRIBUTE>
<!-- The name given to the attribute -->
<NAME>Lookup_MT(1)</NAME>
<!-- The type of annotation used as attribute -->
<TYPE>Lookup</TYPE>
<!-- Optional: the feature name for the feature used to extract values
for the attribute -->
<FEATURE>majorType</FEATURE>
<!-- The position relative to the instance annotation -->
<POSITION>1</POSITION>
<!-- The list of permitted values.
if present, marks a nominal attribute;
if absent, the attribute is numeric (double)
<VALUES>
<!-- One permitted value -->
<VALUE>address</VALUE>
<VALUE>cdg</VALUE>
<VALUE>country_adj</VALUE>
<VALUE>currency_unit</VALUE>
<VALUE>date</VALUE>
<VALUE>date_key</VALUE>
<VALUE>date_unit</VALUE>
<VALUE>facility</VALUE>
<VALUE>facility_key</VALUE>
<VALUE>facility_key_ext</VALUE>
<VALUE>govern_key</VALUE>
<VALUE>greeting</VALUE>
<VALUE>ident_key</VALUE>
-->
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<VALUE>jobtitle</VALUE>
<VALUE>loc_general_key</VALUE>
<VALUE>loc_key</VALUE>
<VALUE>location</VALUE>
<VALUE>number</VALUE>
<VALUE>org_base</VALUE>
<VALUE>org_ending</VALUE>
<VALUE>org_key</VALUE>
<VALUE>org_pre</VALUE>
<VALUE>organization</VALUE>
<VALUE>organization_noun</VALUE>
<VALUE>percent</VALUE>
<VALUE>person_ending</VALUE>
<VALUE>person_first</VALUE>
<VALUE>person_full</VALUE>
<VALUE>phone_prefix</VALUE>
<VALUE>sport</VALUE>
<VALUE>spur</VALUE>
<VALUE>spur_ident</VALUE>
<VALUE>stop</VALUE>
<VALUE>surname</VALUE>
<VALUE>time</VALUE>
<VALUE>time_modifier</VALUE>
<VALUE>time_unit</VALUE>
<VALUE>title</VALUE>
<VALUE>year</VALUE>
</VALUES>
<!-- Optional: if present marks the attribute used as CLASS
Only one attribute can be marked as class -->
</ATTRIBUTE>
<ATTRIBUTE>
<!-- The name given to the attribute -->
<NAME>POS_category(-1)</NAME>
<!-- The type of annotation used as attribute -->
<TYPE>Token</TYPE>
<!-- Optional: the feature name for the feature used to extract values
for the attribute -->
<FEATURE>category</FEATURE>
<!-- The position relative to the instance annotation -->
<POSITION>-1</POSITION>
<!-- The list of permitted values.
if present, marks a nominal attribute;
if absent, the attribute is numeric (double)
<VALUES>
<!-- One permitted value -->
<VALUE>NN</VALUE>
<VALUE>NNP</VALUE>
<VALUE>NNPS</VALUE>
<VALUE>NNS</VALUE>
<VALUE>NP</VALUE>
<VALUE>NPS</VALUE>
<VALUE>JJ</VALUE>
<VALUE>JJR</VALUE>
<VALUE>JJS</VALUE>
<VALUE>JJSS</VALUE>
<VALUE>RB</VALUE>
<VALUE>RBR</VALUE>
<VALUE>RBS</VALUE>
<VALUE>VB</VALUE>
<VALUE>VBD</VALUE>
<VALUE>VBG</VALUE>
<VALUE>VBN</VALUE>
<VALUE>VBP</VALUE>
<VALUE>VBZ</VALUE>
-->
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<VALUE>FW</VALUE>
<VALUE>CD</VALUE>
<VALUE>CC</VALUE>
<VALUE>DT</VALUE>
<VALUE>EX</VALUE>
<VALUE>IN</VALUE>
<VALUE>LS</VALUE>
<VALUE>MD</VALUE>
<VALUE>PDT</VALUE>
<VALUE>POS</VALUE>
<VALUE>PP</VALUE>
<VALUE>PRP</VALUE>
<VALUE>PRP$</VALUE>
<VALUE>PRPR$</VALUE>
<VALUE>RP</VALUE>
<VALUE>TO</VALUE>
<VALUE>UH</VALUE>
<VALUE>WDT</VALUE>
<VALUE>WP</VALUE>
<VALUE>WP$</VALUE>
<VALUE>WRB</VALUE>
<VALUE>SYM</VALUE>
<VALUE>\"</VALUE>
<VALUE>#</VALUE>
<VALUE>$</VALUE>
<VALUE>’</VALUE>
<VALUE>(</VALUE>
<VALUE>)</VALUE>
<VALUE>,</VALUE>
<VALUE>--</VALUE>
<VALUE>-LRB-</VALUE>
<VALUE>.</VALUE>
<VALUE>’’</VALUE>
<VALUE>:</VALUE>
<VALUE>::</VALUE>
<VALUE>‘</VALUE>
</VALUES>
<!-- Optional: if present marks the attribute used as CLASS
Only one attribute can be marked as class -->
</ATTRIBUTE>
<ATTRIBUTE>
<!-- The name given to the attribute -->
<NAME>POS_category(0)</NAME>
<!-- The type of annotation used as attribute -->
<TYPE>Token</TYPE>
<!-- Optional: the feature name for the feature used to extract values
for the attribute -->
<FEATURE>category</FEATURE>
<!-- The position relative to the instance annotation -->
<POSITION>0</POSITION>
<!-- The list of permitted values.
if present, marks a nominal attribute;
if absent, the attribute is numeric (double)
<VALUES>
<!-- One permitted value -->
<VALUE>NN</VALUE>
<VALUE>NNP</VALUE>
<VALUE>NNPS</VALUE>
<VALUE>NNS</VALUE>
<VALUE>NP</VALUE>
<VALUE>NPS</VALUE>
<VALUE>JJ</VALUE>
<VALUE>JJR</VALUE>
<VALUE>JJS</VALUE>
-->
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<VALUE>JJSS</VALUE>
<VALUE>RB</VALUE>
<VALUE>RBR</VALUE>
<VALUE>RBS</VALUE>
<VALUE>VB</VALUE>
<VALUE>VBD</VALUE>
<VALUE>VBG</VALUE>
<VALUE>VBN</VALUE>
<VALUE>VBP</VALUE>
<VALUE>VBZ</VALUE>
<VALUE>FW</VALUE>
<VALUE>CD</VALUE>
<VALUE>CC</VALUE>
<VALUE>DT</VALUE>
<VALUE>EX</VALUE>
<VALUE>IN</VALUE>
<VALUE>LS</VALUE>
<VALUE>MD</VALUE>
<VALUE>PDT</VALUE>
<VALUE>POS</VALUE>
<VALUE>PP</VALUE>
<VALUE>PRP</VALUE>
<VALUE>PRP$</VALUE>
<VALUE>PRPR$</VALUE>
<VALUE>RP</VALUE>
<VALUE>TO</VALUE>
<VALUE>UH</VALUE>
<VALUE>WDT</VALUE>
<VALUE>WP</VALUE>
<VALUE>WP$</VALUE>
<VALUE>WRB</VALUE>
<VALUE>SYM</VALUE>
<VALUE>\"</VALUE>
<VALUE>#</VALUE>
<VALUE>$</VALUE>
<VALUE>’</VALUE>
<VALUE>(</VALUE>
<VALUE>)</VALUE>
<VALUE>,</VALUE>
<VALUE>--</VALUE>
<VALUE>-LRB-</VALUE>
<VALUE>.</VALUE>
<VALUE>’’</VALUE>
<VALUE>:</VALUE>
<VALUE>::</VALUE>
<VALUE>‘</VALUE>
</VALUES>
<!-- Optional: if present marks the attribute used as CLASS
Only one attribute can be marked as class -->
</ATTRIBUTE>
<ATTRIBUTE>
<!-- The name given to the attribute -->
<NAME>POS_category(1)</NAME>
<!-- The type of annotation used as attribute -->
<TYPE>Token</TYPE>
<!-- Optional: the feature name for the feature used to extract values
for the attribute -->
<FEATURE>category</FEATURE>
<!-- The position relative to the instance annotation -->
<POSITION>1</POSITION>
<!-- The list of permitted values.
if present, marks a nominal attribute;
if absent, the attribute is numeric (double)
<VALUES>
-->
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<!-- One permitted value -->
<VALUE>NN</VALUE>
<VALUE>NNP</VALUE>
<VALUE>NNPS</VALUE>
<VALUE>NNS</VALUE>
<VALUE>NP</VALUE>
<VALUE>NPS</VALUE>
<VALUE>JJ</VALUE>
<VALUE>JJR</VALUE>
<VALUE>JJS</VALUE>
<VALUE>JJSS</VALUE>
<VALUE>RB</VALUE>
<VALUE>RBR</VALUE>
<VALUE>RBS</VALUE>
<VALUE>VB</VALUE>
<VALUE>VBD</VALUE>
<VALUE>VBG</VALUE>
<VALUE>VBN</VALUE>
<VALUE>VBP</VALUE>
<VALUE>VBZ</VALUE>
<VALUE>FW</VALUE>
<VALUE>CD</VALUE>
<VALUE>CC</VALUE>
<VALUE>DT</VALUE>
<VALUE>EX</VALUE>
<VALUE>IN</VALUE>
<VALUE>LS</VALUE>
<VALUE>MD</VALUE>
<VALUE>PDT</VALUE>
<VALUE>POS</VALUE>
<VALUE>PP</VALUE>
<VALUE>PRP</VALUE>
<VALUE>PRP$</VALUE>
<VALUE>PRPR$</VALUE>
<VALUE>RP</VALUE>
<VALUE>TO</VALUE>
<VALUE>UH</VALUE>
<VALUE>WDT</VALUE>
<VALUE>WP</VALUE>
<VALUE>WP$</VALUE>
<VALUE>WRB</VALUE>
<VALUE>SYM</VALUE>
<VALUE>\"</VALUE>
<VALUE>#</VALUE>
<VALUE>$</VALUE>
<VALUE>’</VALUE>
<VALUE>(</VALUE>
<VALUE>)</VALUE>
<VALUE>,</VALUE>
<VALUE>--</VALUE>
<VALUE>-LRB-</VALUE>
<VALUE>.</VALUE>
<VALUE>’’</VALUE>
<VALUE>:</VALUE>
<VALUE>::</VALUE>
<VALUE>‘</VALUE>
</VALUES>
<!-- Optional: if present marks the attribute used as CLASS
Only one attribute can be marked as class -->
</ATTRIBUTE>
<ATTRIBUTE>
<!-- The name given to the attribute -->
<NAME>Entity(0)</NAME>
<!-- The type of annotation used as attribute -->
<TYPE>Entity</TYPE>
<!-- The position relative to the instance annotation -->
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<POSITION>0</POSITION>
<CLASS/>
<!-- Optional: if present marks the attribute used as CLASS
Only one attribute can be marked as class -->
</ATTRIBUTE>
</DATASET>
<ENGINE>
<WRAPPER>gate.creole.ml.weka.Wrapper</WRAPPER>
<OPTIONS>
<CLASSIFIER OPTIONS="-S -C 0.25 -B -M 2">weka.classifiers.trees.J48</CLASSIFIER>
<CONFIDENCE-THRESHOLD>0.85</CONFIDENCE-THRESHOLD>
</OPTIONS>
</ENGINE>
</ML-CONFIG>
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Chapter 18
Tools for Alignment Tasks
18.1
Introduction
This chapter introduces a new plugin called ‘Alignment’ that comprises of tools to perform
text alignment at various level (e.g word, phrase, sentence etc). It allows users to integrate
other tools that can be useful for speeding up the alignment process.
Text alignment can be achieved at a document, section, paragraph, sentence and a word level.
Given two parallel corpora, where the first corpus contains documents in a source language
and the other in a target language, the first task is to find out the parallel documents and
align them at the document level. For these tasks one would need to refer to more than one
document at the same time. Hence, a need arises for Processing Resources (PRs) which can
accept more than one document as parameters. For example given two documents, a source
and a target, a Sentence Alignment PR would need to refer to both of them to identify
which sentence of the source document aligns with which sentence of the target document.
However, the problem occurs when such a PR is part of a corpus pipeline. In a corpus
pipeline, only one document from the selected corpus at a time is set on the member PRs.
Once the PRs have completed their execution, the next document in the corpus is taken and
set on the member PRs. Thus it is not possible to use a corpus pipeline and at the same
time supply for than one document to the underlying PRs.
18.2
The Tools
We have introduced a few new resources in GATE that allows processing parallel data.
These include resources such as CompoundDocument, CompositeDocument, and a new
AlignmentEditor to name a few. Below we describe these components. Please note that
all these resources are distributed as part of the ‘Alignment’ plugin and therefore the users
should load the plugin first in order to use these resources.
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18.2.1
Compound Document
A new Language Resource (LR), called CompoundDocument, is introduced which is a collection of documents and allow various documents to be grouped together under a single
document. The CompoundDocument allows adding more documents to it and removing
them if required. It implements the gate.Document interface allowing users to carry out
all operations that can be done on a normal gate document. For example, if a PR such as
Sentence Aligner needs access to two documents (e.g. source and target documents), these
documents can be grouped under a single compound document and supplied to the Sentence
Alignment PR.
To instantiate CompoundDocument user needs to provide the following parameters.
• encoding - encoding of the member documents. All document members must have the
same encoding (e.g. Unicode, UTF-8, UTF-16).
• collectRepositioningInfo - this parameter indicates whether the underlying documents
should collect the repositioning information in case the contents of these documents
change.
• preserveOriginalContent - if the original content of the underlying documents should
be preserved.
• documentIDs - users need to provide a unique ID for each document member. These
ids are used to locate the appropriate documents.
• sourceUrl - given a URL of one of the member documents, the instance of CompoundDocument searches for other members in the same folder based on the ids provided
in the documentIDs parameter. Following document name conventions are followed to
search other member documents:
– FileName.id.extension (filename followed by id followed the extension and all of
these separated by a ‘.’ (dot)).
– For example if user provides three document IDs (e.g. ‘en’, ‘hi’ and ‘gu’) and
selects a file with name ‘File.en.xml’, the CompoundDocument will search for
rest of the documents (i.e. ‘File.hi.xml’ and ‘File.gu.xml’). The file name (i.e.
‘File’) and the extension (i.e. ‘xml’) remain common for all three members of the
compound document.
Figure 18.1 shows a snapshot for instantiating a compound document from GATE Developer.
Compound document provides various methods that help in accessing their individual members.
public Document getDocument(String docid);
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Figure 18.1: Compound Document
The following method returns a map of documents where the key is a document ID and the
value is its respective document.
public Map getDocuments();
Please note that only one member document in a compound document can have focus set
on it. Then all the standard document methods of gate.Document interface apply to the
document with focus set on it. For example, if there are two documents, ‘hi’ and ‘en’, and
the focus is set on the document ‘hi’ then the getAnnotations() method will return a default
annotation set of the ‘hi’ document. One can use the following method to switch the focus
of a compound document to a different document:
public void setCurrentDocument(String documentID);
public Document getCurrentDocument();
As explained above, new documents can be added to or removed from the compound document using the following method:
public void addDocument(String documentID, Document document);
public void removeDocument(String documentID);
The following code snippet demonstrates how to create a new compound document using
GATE Embedded:
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1
2
3
// step 1: initialize GATE
Gate . init ();
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5
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7
// step 2: load the Alignment plugin
File alignmentHome = new File ( Gate . getPluginsHome () , " Alignment " );
Gate . ge tCreol eRegis ter (). addDirectory ( alignmentHome . toURL ());
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// step 3: set the parameters
FeatureMap fm = Factory . newFeatureMap ();
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// for example you want to create a compound document for
// File.id1.xml and File.id2.xml
List docIDs = new ArrayList ();
docIDs . add ( " id1 " );
docIDs . add ( " id2 " );
fm . put ( " documentIDs " , docIDs );
fm . put ( " sourceUrl " , new URL ( " file :/// url / to / File . id1 . xml " ));
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// step 4: finally create an instance of compound document
Document aDocument = ( gate . compound . CompoundDocument )
Factory . createResource ( " gate . compound . impl . C o m p o u n d D o c u m e n t I m p l " , fm );
18.2.2
CompoundDocumentFromXml
As described later in the chapter, the entire compound document can be saved in a single
xml file. In order to load such a compound document from the saved xml file, we provide
a language resource called CompoundDocumentFromXml. This is same as the Compound
Document. The only difference is in the parameters needed to instantiate this resource. This
LR requires only one parameter called compoundDocumentUrl. The parameter is the url to
the xml file.
18.2.3
Compound Document Editor
The compound document editor is a visual resource (VR) associated with the compound
document. The VR contains several tabs - each representing a different member of the
compound document. All standard functionalities such as GATE document editor, with all
its add-on plugins such as AnnotationSetView, AnnotationsList, coreference editor etc., are
available to be used with each individual member.
Figure 18.2 shows a compound document editor with English and Hindi documents as members of the compound document.
As shown in the figure 18.2, there are several buttons at the top of the editor that provide different functionalities. For instance, the Add button, allows adding a new member document
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Figure 18.2: Compound Document Editor
to the compound document. The Remove button removes the current visible member from
the document. The buttons Save and Save As XML allow saving the documents individually
and in a single xml document respectively. The Switch button allows changing focus of the
compound document from one member to the other (this functionality is explained later).
Finally, the Alignment Editor allows one to start the alignment editor to align text.
18.2.4
Composite Document
The composite document allows users to merge the texts of member documents and keep the
merged text linked with their respective member documents. In other words, if users make
any change to the composite document (e.g. add new annotations or remove any existing
annotations), the relevant effect is made to their respective documents.
A PR called CombineMembersPR allows creation of a new composite document. It asks for
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a class name that implements the CombiningMethod interface. The CombiningMethod tells
the CombineMembersPR how to combine texts and create a new composite document.
For example, a default implementation of the CombiningMethod, called DefaultCombiningMethod, takes the following parameters and puts the text of the compound document’s
members into a new composite document.
unitAnnotationType=Sentence
inputASName=Key
copyUnderlyingAnnotations=true;
The first parameter tells the combining method that it is the ‘Sentence’ annotation type
whose text needs to be merged and it should be taken from the ‘Key’ annotation set (second
parameter) and finally all the underlying annotations of every Sentence annotation must be
copied in the composite document.
If there are two members of a compound document (e.g. ‘hi’ and ‘en’), given the above
parameters, the combining method finds out all the annotations of type Sentence from each
document and sorts them in ascending order, and one annotation from each document is
put one after another in a composite document. This operation continues until all the
annotations have been traversed.
Document en
Sen1
Sen2
Sen3
Document hi
Shi1
Shi2
Shi3
Document Composite
Sen1
Shi1
Sen2
Shi2
Sen3
Shi3
The composite document also maintains a mapping of text offsets such that if someone adds
a new annotation to or removes any annotation from the composite document, they are
added to or removed from their respective documents. Finally the newly created composite
document becomes a member of the same compound document.
18.2.5
DeleteMembersPR
This PR allows deletion of a specific member of the compound document. It takes a parameter called ‘documentID’ and deletes a document with this name.
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18.2.6
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SwitchMembersPR
As described above, only one member of the compound document can have focus set on it.
PRs trying to use the getDocument() method get a pointer to the compound document;
however all the other methods of the compound document give access to the information
of the document member with the focus set on it. So if user wants to process a particular
member of the compound document with some PRs, s/he should use the SwitchMembersPR
that takes one parameter called documentID and sets focus to the document with that
specific id.
18.2.7
Saving as XML
Calling the toXml() method on a compound document returns the XML representation of
the member which has focus. However, GATE Developer provides an option to save all
member documents in different files. This option appears in the options menu when the
user right-clicks on the compound document. The user is asked to provide a name for the
directory in which all the members of the compound document will be saved in separate
files.
It is also possible to save all members of the compound document in a single XML file. The
option, ‘Save in a single XML Document’, also appears in the options menu. After saving it
in a single XML document, the user can use the option ‘Compound Document from XML’
to load the document back into GATE Developer.
18.2.8
Alignment Editor
Inspired by various tools, we have implemented a new version of alignment editor that is
comprised of several new features. We preserve standard ways of aligning text but at the
same time provide advanced features that can be used for facilitating incremental learning.
The alignment editor can be used for performing alignment at any annotation level. When
performing alignment at word or sentence level, the texts being aligned need to be preprocessed in order to identify tokens and sentences boundaries.
Information about the alignments carried over the text of a compound document is stored
as a document feature in the compound document itself. Since the document features are
stored in a map, every object stored as a document feature needs to have a unique name that
identifies that feature. There is no limit on how many features one can store provided they
all have different names. This allows storing alignment information, carried out at different
levels, in separate alignment instances. For example, if a user is carrying out alignment
at a word level, he/she can store it in an alignment object with a name word-alignment.
Similarly, sentence alignment information can be stored with a name sentence-alignment.
If multiple users are annotating the same document, alignments produced by different users
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can be stored with different names (e.g. word-alignment-user1, word-alignment-user2
etc.). Alignment objects can be used for:
• aligning and unaligning two annotations;
• checking if the two annotations are aligned with each other;
• obtaining all the aligned annotations in a document;
• obtaining all the annotations that are aligned to a particular annotation.
Given a compound document containing a source and a target document, the alignment
editor starts in the alignment viewer mode. In this mode the texts of the two documents are
shown side-by-side in parallel windows. The purpose of the alignment viewer is to highlight
the annotations that are already aligned. The figure 18.3 shows the alignment viewer. In
this case the selected documents are English and Hindi, titled as en and hi respectively.
Figure 18.3: Alignment Viewer
To see alignments, user needs to select the alignment object that he/she wants to see alignments from. Along with this, user also needs to select annotation sets - one for the source
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document and one for the target document. Given these parameters, the alignment viewer
highlights the annotations that belong to the selected annotation sets and have been aligned
in the selected alignment object. When the mouse is placed on one of the aligned annotations, the selected annotation and the annotations that are aligned to the selected annotation
are highlighted in red. In this case (see figure 18.3) the word go is aligned with the words
chalate hein.
Before the alignment process can be started, the tool needs to know few parameters about
the alignment task.
Unit Of Alignment: this is the annotation type that users want to perform alignment
at. Data Source: generally, if performing a word alignment task, people consider a pair
of aligned sentences one at a time and align words within sentences. If the sentences are
annotated, for example as Sentence, the Sentence annotation type is called Parent of
Unit of Alignment. The Data Source contains information about the aligned parents
of unit of alignment. In this case, it would refer to the alignment object that contains
alignment information about the annotations of type Sentence. The editor iterates through
the aligned sentences and forms pairs of parent of unit of alignments to be shown to the
user one by one. If user does not provide any data source, a single pair is formed containing
entire documents. Alignment Feature Name: this is the name given to the alignment
object where the information about new alignments is stored.
The purpose of the alignment viewer is to highlight the annotations that are already aligned.
The editor comes with three different views for performing alignment which the user can
select at the time of creating a new alignment task: the Links view (see 18.4 - suitable for
character, word and phrase level alignments), the Parallel view (see 18.5 - suitable for
annotations which have longer texts, e.g. sentences, paragraphs, sections) and the Matrix
view (see 18.6) - suitable for character, word and phrase level alignment.
Let us assume that the user wants to align words in sentences using the Links view. The
first thing he needs to do is to create a new Alignment task. This can be achieved by
clicking on the File menu and selecting the New Task option. User is asked to provide
certain parameters as discussed above. The editor also allows to store task configurations in
an xml file which can be at later stage reloaded in the alignment editor. Also, if there are
more than one task created, the editor allows users to switch between them.
To align one or more words in the source language with one or more words in the target
language, the user needs to select individual words by clicking on them individually. Clicking
on words highlights them with an identical colour. Right clicking on any of the selected words
brings up a menu with the two default options: Reset Selection and Align. Different
colours are used for highlighting different pairs of alignments. This helps distinguishing one
set of aligned words from other sets of aligned pairs. Also a link between the aligned words
in the two texts is drawn to show the alignment. To unalign, user needs to right click on
the aligned words and click on the Remove Alignment option. Only the word on which user
right-clicks is taken out of the alignment and rest of the words in the pair remain unaffected.
We use the term Orphaned Annotation to refer to the annotation which does not have any
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alignment in the target document. If after removing an annotation from alignment pair,
there are any orphaned annotations in the alignment pair, they are unaligned too.
Figure 18.4: Links View
Advanced Features
The options Align, Reset Selection and Remove Alignment are available by default. The
Align and the Reset Selection options appear when user wants to align new annotations.
The Remove Alignment option only appears when user right clicks on the already aligned
annotations. The first two actions are available when there is at least one annotation selected
in the source language and another one is selected in the target language. Apart from these
three basic actions, the editor also allows adding more actions to the editor.
There are four different types of actions: actions that should be taken before the user
starts aligning words (PreDisplayAction); actions that should be taken when the user
aligns annotations (AlignmentAction); the actions that should be taken when the user has
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Figure 18.5: Parallel View
completed aligning all the words in the given sentence pair (FinishedAlignmentAction)
and the actions to publish any data or statistics to the user. For example, to help users
in the alignment process by suggesting word alignments, one may want to wrap a pretrained statistical word alignment model as PreDisplayAction. Similarly, actions of the type
AlignmentAction can be used for submitting examples to the model in order for the model
to update itself. When all the words in a sentence pair are aligned, one may want to sign off
the pair and take actions such as comparing all the alignments in that sentence pair with the
alignments carried out by some other user for the same pair. Similarly, while collecting data
in the background, one might want to display some information to the user (e.g. statistics
for the collected data or some suggestions that help users in the alignment process).
When users click on the next or the previous button, the editor obtains the next or the
previous pair that needs to be shown from the data source. Before the pair is displayed in the
editor, the editor calls the registered instances of the PreDisplayAction and the current pair
object is passed onto the instances of PreDisplayAction. Please note that this only happens
when the pair is not already signed off. Once the instances of PreDisplayAction have been
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Figure 18.6: Matrix View
executed, the editor collects the alignment information from the compound document and
displays it in the editor.
As explained earlier, when users right click on units of alignment in the editor a popup menu
with default options (e.g. Align, Reset Selection and Remove Alignment) is shown. The
editor allows adding new actions to this menu. It is also possible that users may want to
take extra actions when they click on any of the Align or the Remove Alignment options.
The AlignmentAction makes it possible to achieve this. Below we list some of the parameters
of the AlignmenAction. The implementation is called depending on these parameters.
• invokeForAlignedAnnotation - the action appears in the options menu when user
right clicks on the aligned annotation.
• invokeForHighlightedUnalignedAnnotation - the action appears in the options
menu when user right clicks on a highlighted but unaligned annotation.
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• invokeForUnhighlightedUnalignedAnnotation - the action appears in the options
menu when user right clicks on an unhighlighted and unaligned annotation.
• invokeWithAlignAction - the action is executed whenever user aligns some annotations.
• invokeWithRemoveAction - the action is executed whenever user removes any alignment.
• caption - in case of the first three options, the caption is used in the options menu.
In case of the fourth and the fifth options, the caption appears as a check box under
the actions tab.
These methods can be used for, for example, building up a dictionary in the background
while aligning word pairs. Before users click on the next button, they are asked if the pair
they were aligning has been aligned completely (i.e. signed off for further alignment). If user
replies yes to it, the actions registered as FinishedAlignmentAction are executed one after
the other. This could be helpful, for instance, to write an alignment exporter that exports
alignment results in an appropriate format or to update the dictionary with new alignments.
Users can point the editor to a file that contains a list of actions and parameters needed to
initialize them. A configuration file is a simple text file with fully-qualified class name, and
required parameters specified in it. Below we give an example of such a configuration file.
gate.alignment.actions.AlignmentCache,$relpath$/align-cache.txt,root
The first argument is the name of the class that implements one of the actions described
above. The second parameter is the name of the file in which the alignment cache should store
its results. Finally, the third argument instructs the alignment cache to store root forms of
the words in the dictionary so that different forms of the same words can be matched easily.
All the parameters (comma separated) after the class name are passed to the action. The
relpath parameter is resolved at runtime.
AlignmentCache is one such example of FinishedAlignmentAction and the PreDisplayAction. This is an inbuilt alignment cache in the editor which collects alignment pairs that the
users annotate. The idea here is to cache such pairs and later, align them automatically if
they appear in subsequent pairs, thus reducing the efforts of humans to annotate the same
pair again. By default the alignment cache is disabled. Users wishing to enable it should look
into the plugins/Alignment/resources/actions.conf and uncomment the appropriate line.
Users wishing to implement their own actions should refer to the implementation of the
AlignmentCache.
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18.2.9
Tools for Alignment Tasks
Saving Files and Alignments
A compound document can have more than one member documents, the alignment information stored as a document feature and more than one alignment features. The framework
allows users to store the whole compound document in a single XML file. The XML file contains all the necessary information about the compound document to load it back in Gate
and bring it to the state the compound document was when saving the document as XML. It
contains XML produced for each and every member document of the compound document
and the details of the document features set on the compound document. XML for each
member document includes XML elements for its content; annotations sets and annotations;
document features set on individual member document and the id given to this document as
as member of the compound document. Having a single XML file makes it possible to port
the entire file from one destination to the others easily.
Apart from this, the framework has an alignment exporter. Using the alignment exporter, it
is possible to store the alignment information in a separate XML file. For example, once the
annotators have aligned documents at a word level, the alignment information about both
the unit and the parent of unit annotations can be exported to an XML file. Figure 18.7
shows an XML file with word alignment information in it.
Figure 18.7: Word Alignment XML File
When aligning words in sentences, it is possible to have one or more source sentences aligned
with one or more target sentences in a pair. This is achieved by having Source and Target
elements within the Pair element which can have one or more Sentence elements in each
of them. Each word or token within these sentences is marked with Token element. Every
Token element has a unique id assigned to it which is used when aligning words. It is
possible to have 1:1 or 1:many and many:1 alignments. The Alignment element is used for
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mentioning every alignment pair with source and target attributes that refer to one of the
source token ids and one of the target document ids respectively. For example, according
to the first alignment entry, the source token markets with id 3 is aligned with the target
token bAzAr with id 3. The exporter does not export any entry for the unaligned words.
18.2.10
Section-by-Section Processing
In this section, we describe a component that allows processing documents section-by-section.
Processing documents this way is useful for many reasons:
For example, a patent document has several different sections but user is interested in processing only the ‘claims’ section or the ‘technical details section’. This is also useful for
processing a large document where processing it as a single document is not possible and the
only alternative is to divide it in several small documents to process them independently.
However, doing so would need another process that merges all the small documents and their
annotations back into the original document. On the other hand, a webpage may contain
profiles of different people. If the document has more than one person with similar names,
running the ‘Orthomatcher PR’ on such a document would produce incorrect coreference
chains.
All such problems can be solved by using a PR called ‘Segment Processing PR’. This PR
is distributed as part of the ‘Alignment’ plugin. User needs to provide the following four
parameters to run this PR.
1. document: This is the document to be processed.
2. controller: This is a corpus controller that needs to be used for processing the
segments of the document.
3. segmentAnnotationType: Sections of the documents (that need to be processed)
should be annotated with some annotation type and the type of such annotation should
be provided as the value to this parameter.
4. inputASName: This is the name of the annotation set that contains the segment annotations.
Given these parameters, each span in the document that is annotated as the type specified
by the segmentAnnotationType is processed independently.
Given a corpus of publications, if you just want to process the abstract section with the
ANNIE application, please follow the following steps. It is assumed that the boundaries of
abstracts in all these publications are already identified. If not, you would have to do some
processing to identify them prior to using the following steps. In the following example,
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we assume that the abstract boundaries have been annotated as ‘Abstract’ annotations and
stored under the ‘Original markups’ annotation set.
Steps:
1. Create a new corpus and populate it with a set of publications that you would like to
process with ANNIE.
2. Load the ANNIE application.
3. Load the ‘Alignment’ plugin.
4. Create an instance of the ‘Segment Processing PR’ by selecting it from the list of
processing resources.
5. Create a corpus pipeline.
6. Add the ‘Segment Processing PR’ into the pipeline and provide the following parameters:
(a) Provide the corpus with publication documents in it as a parameter to the corpus
controller.
(b) Select the ‘ANNIE’ controller for the ‘controller’ parameter.
(c) Type ‘Abstract’ in the ‘segmentAnnotationType’ parameter.
(d) Type ‘Original markups’ in the ‘inputASName’ parameter.
7. Run the application.
Now, you should see that the ANNIE application has only processed the text in each document that was annotated as ‘Abstract’.
Chapter 19
Combining GATE and UIMA
UIMA (Unstructured Information Management Architecture) is a platform for natural language processing, originally developed by IBM but now maintained by the Apache Software
Foundation. It has many similarities to the GATE architecture – it represents documents
as text plus annotations, and allows users to define pipelines of analysis engines that manipulate the document (or Common Analysis Structure in UIMA terminology) in much the
same way as processing resources do in GATE. The Apache UIMA SDK provides support
for building analysis components in Java and C++ and running them either locally on one
machine, or deploying them as services that can be accessed remotely. The SDK is available
for download from http://incubator.apache.org/uima/.
Clearly, it would be useful to be able to include UIMA components in GATE applications
and vice-versa, letting GATE users take advantage of UIMA’s flexible deployment options
and UIMA users access JAPE and the many useful plugins already available in GATE. This
chapter describes the interoperability layer provided as part of GATE to support this. The
UIMA-GATE interoperability layer is based on Apache UIMA 2.2.2. GATE 5.0 and earlier
included an implementation based on version 1.2.3 of the pre-Apache IBM UIMA SDK.
The rest of this chapter assumes that you have at least a basic understanding of core UIMA
concepts, such as type systems, primitive and aggregate analysis engines (AEs), feature structures, the format of AE XML descriptors, etc. It will probably be helpful to refer to the
relevant sections of the UIMA SDK User’s Guide and Reference (supplied with the SDK)
alongside this document.
There are two main parts to the interoperability layer:
1. A wrapper to allow a UIMA Analysis Engine (AE), whether primitive or aggregate, to
be used within GATE as a Processing Resource (PR).
2. A wrapper to allow a GATE processing pipeline (specifically a CorpusController) to
be used within UIMA as an AE.
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Combining GATE and UIMA
The two components operate in very similar ways. Given a document in the source form
(either a GATE Document or a UIMA CAS), a document in the target form is created with a
copy of the source document’s text. Some of the annotations from the source are transferred
to the target, according to a mapping defined by the user, and the target component is then
run. Finally, some of the annotations on the updated target document are then transferred
back to the source, according to the user-defined mapping.
The rest of this document describes this process in more detail. Section 19.1 describes the
GATE AE wrapper, and Section 19.2 describes the UIMA CorpusController wrapper.
19.1
Embedding a UIMA AE in GATE
Embedding a UIMA analysis engine in a GATE application is a two step process. First, you
must construct a mapping descriptor XML file to define how to map annotations between
the UIMA CAS and the GATE Document. This mapping file, along with the analysis engine
descriptor, is used to instantiate an AnalysisEnginePR which calls the analysis engine on an
appropriately initialized CAS. Examples of all the XML files discussed in this section are
available in examples/conf under the UIMA plugin directory.
19.1.1
Mapping File Format
Figure 19.1 shows the structure of a mapping descriptor. The inputs section defines how
annotations on the GATE document are transferred to the UIMA CAS. The outputs section defines how annotations which have been added, updated and removed by the AE are
transferred back to the GATE document.
Input Definitions
Each input definition takes the following form:
<uimaAnnotation type="uima.Type" gateType="GATEType" indexed="true|false">
<feature name="..." kind="string|int|float|fs">
<!-- element defining the feature value goes here -->
</feature>
...
</uimaAnnotation>
When a document is processed, this will create one UIMA annotation of type uima.Type in
the CAS for each GATE annotation of type GATEType in the input annotation set, covering
the same offsets in the text. If indexed is true, GATE will keep a record of which GATE
Combining GATE and UIMA
<uimaGateMapping>
<inputs>
<uimaAnnotation type="..." gateType="..." indexed="true|false">
<feature name="..." kind="string|int|float|fs">
<!-- element defining the feature value goes here -->
</feature>
...
</uimaAnnotation>
</inputs>
<outputs>
<added>
<gateAnnotation type="..." uimaType="...">
<feature name="...">
<!-- element defining the feature value goes here -->
</feature>
...
</gateAnnotation>
</added>
<updated>
...
</updated>
<removed>
...
</removed>
</outputs>
</uimaGateMapping>
Figure 19.1: Structure of a mapping descriptor for an AE in GATE
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Combining GATE and UIMA
annotation gave rise to which UIMA annotation. If you wish to be able to track updates
to this annotation’s features and transfer the updated values back into GATE, you must
specify indexed="true". The indexed attribute defaults to false if omitted.
Each contained feature element will cause the corresponding feature to be set on the generated annotation. UIMA features can be string, integer or float valued, or can be a reference
to another feature structure, and this must be specified in the kind attribute. The feature’s
value is specified using a nested element, but exactly how this value is handled is determined
by the kind.
There are various options for setting feature values:
• <string value="fixed string" /> The simplest case - a fixed Java String.
• <docFeatureValue name="featureName" /> The value of the given named feature of
the current GATE document.
• <gateAnnotFeatureValue name="featureName" /> The value of a given feature on
the current GATE annotation (i.e. the one on which the offsets of the UIMA annotation
are based).
• <featureStructure type="uima.fs.Type">...</featureStructure> A feature structure of the given type. The featureStructure element can itself contain feature
elements recursively.
The value is assigned to the feature according to the feature’s kind:
string The value object’s toString() method is called, and the resulting String is set as
the string value of the feature.
int If the value object is a subclass of java.lang.Number, its intValue() method is
called, and the result is set as the integer value of the feature. If the value object is not a Number, it is toString()ed, and the resulting String is parsed using
Integer.parseInt(). If this succeeds, the integer result is used, if it fails the feature
is set to zero.
float As for int, except that Numbers are converted by calling floatValue(), and nonNumbers are parsed using Float.parseFloat().
fs The value object is assumed to be a FeatureStructure, and is used as-is.
ClassCastException will result if the value object is not a FeatureStructure.
A
In particular, <featureStructure> value elements should only be used with features of kind
fs. While nothing will stop you using them with string features, the result will probably
not be what you expected.
Combining GATE and UIMA
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Output Definitions
The output definitions take a similar form. There are three groups:
added Annotations which have been added by the AE, and for which corresponding new
annotations are to be created in the GATE document.
updated Annotations that were created by an input definition (with indexed="true")
whose feature values have been modified by the AE, and these values are to be transferred back to the original GATE annotations.
removed Annotations that were created by an input definition (with indexed="true")
which have been removed from the CAS1 and whose source annotations are to be
removed from the GATE document.
The definition elements for these three types all take the same form:
<gateAnnotation type="GATEType" uimaType="uima.Type">
<feature name="featureName">
<!-- element defining the feature value goes here -->
</feature>
...
</gateAnnotation>
For added annotations, this has the mirror-image effect to the input definition – for each
UIMA annotation of the given type, create a GATE annotation at the same offsets and set
its feature values as specified by feature elements. For a gateAnnotation the feature
elements do not have a kind, as features in GATE can have arbitrary Objects as values.
The possible feature value elements for a gateAnnotation are:
• <string value="fixed string" /> A fixed string, as before.
• <uimaFSFeatureValue name="uima.Type:FeatureName" kind="string|int|float" />
The value of the given feature of the current UIMA annotation. The feature name must
be specified in fully-qualified form, including the type on which it is defined. The kind
is used in a similar way as in input definitions:
string The Java String object returned as the string value of the feature is used.
int An Integer object is created from the integer value of the feature.
float A Float object is created from the float value of the feature.
1
Strictly speaking, removed from the annotation index, as feature structures cannot be removed from the
CAS entirely.
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fs The UIMA FeatureStructure object is returned. Since FeatureStructure objects are not guaranteed to be valid once the CAS has been cleared, a downstream GATE component must extract the relevant information from the feature
structure before the next document is processed. You have been warned.
Feature names in uimaFSFeatureValue must be qualified with their type name, as the feature
may have been defined on a supertype of the feature’s own type, rather than the type itself.
For example, consider the following:
<gateAnnotation type="Entity" uimaType="com.example.Entity">
<feature name="type">
<uimaFSFeatureValue name="com.example.Entity:Type" kind="string" />
</feature>
<feature name="startOffset">
<uimaFSFeatureValue name="uima.tcas.Annotation:begin" kind="int" />
</feature>
</gateAnnotation>
For updated annotations, there must have been an input definition with indexed="true"
with the same GATE and UIMA types. In this case, for each GATE annotation of the
appropriate type, the UIMA annotation that was created from it is found in the CAS. The
feature definitions are then used as in the added case, but here, the feature values are set on
the original GATE annotation, rather than on a newly created annotation.
For removed annotations, the feature definitions are ignored, and the annotation is removed
from GATE if the UIMA annotation which it gave rise to has been removed from the UIMA
annotation index.
A Complete Example
Figure 19.2 shows a complete example mapping descriptor for a simple UIMA AE that takes
tokens as input and adds a feature to each token giving the number of lower case letters in
the token’s string.2 In this case the UIMA feature that holds the number of lower case letters
is called LowerCaseLetters, but the GATE feature is called numLower. This demonstrates
that the feature names do not need to agree, so long as a mapping between them can be
defined.
19.1.2
The UIMA Component Descriptor
As well as the mapping file, you must provide the UIMA component descriptor that defines
how to access the AE that is to be called. This could be a primitive or aggregate analysis
2
The Java code implementing this AE is in the examples directory of the UIMA plugin. The AE descriptor
and mapping file are in examples/conf.
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431
<uimaGateMapping>
<inputs>
<uimaAnnotation type="gate.uima.cas.Token" gateType="Token" indexed="true">
<feature name="String" kind="string">
<gateAnnotFeatureValue name="string" />
</feature>
</uimaAnnotation>
</inputs>
<outputs>
<updated>
<gateAnnotation type="Token" uimaType="gate.uima.cas.Token">
<feature name="numLower">
<uimaFSFeatureValue name="gate.uima.cas.Token:LowerCaseLetters"
kind="int" />
</feature>
</gateAnnotation>
</updated>
</outputs>
</uimaGateMapping>
Figure 19.2: An example mapping descriptor
engine descriptor, or a URI specifier giving the location of a remote Vinci or SOAP service.
It is up to the developer to ensure that the types and features used in the mapping descriptor
are compatible with the type system and capabilities of the AE, or a runtime error is likely
to occur.
19.1.3
Using the AnalysisEnginePR
To use a UIMA AE in GATE Developer, load the UIMA plugin and create a ‘UIMA Analysis
Engine’ processing resource. If using the GATE Embedded, rather than GATE Developer,
the class name is gate.uima.AnalysisEnginePR. The processing resource expects two parameters:
analysisEngineDescriptor The URL of the UIMA analysis engine descriptor (or URI
specifier, for a remote AE service). This must be a file: URL, as UIMA needs a file
path against which to resolve imports.
mappingDescriptor The URL of the mapping descriptor file. This may be any kind of
URL (file:, http:, Class.getResource(), ServletContext.getResource(), etc.)
Any errors processing either of the descriptor files will cause an exception to be thrown.
Once instantiated, you can add the PR to a pipeline in the usual way. AnalysisEnginePR
implements LanguageAnalyser, so can be used in any of the standard GATE pipeline types.
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Combining GATE and UIMA
The PR takes the following runtime parameter (in addition to the document parameter which
is set automatically by a CorpusController):
annotationSetName The annotation set to process. Any input mappings take annotations
from this set, and any output mappings place their new annotations in this set (added
outputs) or update the input annotations in this set (updated or removed). If not
specified, the default (unnamed) annotation set is used.
The Annotator implementation must be available for GATE to load. For an annotator
written in Java, this means that the JAR file containing the annotator class (and any other
classes it depends on) must be present in the GATE classloader. The easiest way to achieve
this is to put the JAR file or files in a new directory, and create a creole.xml file in the
same directory to reference the JARs:
<CREOLE-DIRECTORY>
<JAR>my-annotator.jar</JAR>
<JAR>classes-it-uses.jar</JAR>
</CREOLE-DIRECTORY>
This directory should then be loaded in GATE as a CREOLE plugin. Note that, due to the
complex mechanics of classloaders in Java, putting your JARs in GATE’s lib directory will
not work.
For annotators written in C++ you need to ensure that the C++ enabler libraries (available
separately from http://incubator.apache.org/uima/) and the shared library containing your
annotator are in a directory which is on the PATH (Windows) or LD LIBRARY PATH (Linux)
when GATE is run.
19.2
Embedding a GATE CorpusController in UIMA
The process of embedding a GATE controller in a UIMA application is more or less the
mirror image of the process detailed in the previous section. Again, the developer must
supply a mapping descriptor defining how to map between UIMA and GATE annotations,
and pass this, plus the GATE controller definition, to an AE which performs the translation
and calls the GATE controller.
19.2.1
Mapping File Format
The mapping descriptor format is virtually identical to that described in Section 19.1.1,
except that the input definitions are <gateAnnotation> elements and the output definitions
are <uimaAnnotation> elements. The input and output definition elements support an
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433
extra attribute, annotationSetName, which allows inputs to be taken from, and outputs
to be placed in, different annotation sets. For example, the following hypothetical example
maps com.example.Person annotations into the default set and com.example.html.Anchor
annotations to ‘a’ tags in the ‘Original markups’ set.
<inputs>
<gateAnnotation type="Person" uimaType="com.example.Person">
<feature name="kind">
<uimaFSFeatureValue name="com.example.Person:Kind" kind="string"/>
</feature>
</gateAnnotation>
<gateAnnotation type="a" annotationSetName="Original markups"
uimaType="com.example.html.Anchor">
<feature name="href">
<uimaFSFeatureValue name="com.example.html.Anchor:hRef" kind="string" />
</feature>
</gateAnnotation>
</inputs>
Figure 19.3 shows a mapping descriptor for an application that takes tokens and sentences
produced by some UIMA component and runs the GATE part of speech tagger to tag them
with Penn TreeBank POS tags.3 In the example, no features are copied from the UIMA
tokens, but they are still indexed="true" as the POS feature must be copied back from
GATE.
19.2.2
The GATE Application Definition
The GATE application to embed is given as a standard ‘.gapp file’, as produced by saving the
state of an application in the GATE GUI. The .gapp file encodes the information necessary
to load the correct plugins and create the various CREOLE components that make up the
application. The .gapp file must be fully specified and able to be executed with no user
intervention other than pressing the Go button. In particular, all runtime parameters must
be set to their correct values before saving the application state. Also, since paths to things
like CREOLE plugin directories, resource files, etc. are stored relative to the .gapp file’s
location, you must not move the .gapp file to a different directory unless you can keep
all the CREOLE plugins it depends on at the same relative locations. The ‘Export for
GATECloud.net’ option (section 3.8.4) may help you here.
3
The .gapp file implementing this example is in the test/conf directory under the UIMA plugin, along
with the mapping file and the AE descriptor that will run it.
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Combining GATE and UIMA
<uimaGateMapping>
<inputs>
<gateAnnotation type="Token"
uimaType="com.ibm.uima.examples.tokenizer.Token"
indexed="true" />
<gateAnnotation type="Sentence"
uimaType="com.ibm.uima.examples.tokenizer.Sentence" />
</inputs>
<outputs>
<updated>
<uimaAnnotation type="com.ibm.uima.examples.tokenizer.Token"
gateType="Token">
<feature name="POS" kind="string">
<gateAnnotFeatureValue name="category" />
</feature>
</uimaAnnotation>
</updated>
</outputs>
</uimaGateMapping>
Figure 19.3: An example mapping descriptor for the GATE POS tagger
19.2.3
Configuring the GATEApplicationAnnotator
GATEApplicationAnnotator is the UIMA annotator that handles mapping the CAS into a
GATE document and back again and calling the GATE controller. There is a template AE
descriptor XML file for the annotator provided in the conf directory. Most of the template
file can be used unchanged, but you will need to modify the type system definition and
input/output capabilities to match the types and features used in your mapping descriptor.
If the mapping descriptor references a type or feature that is not defined in the type system,
a runtime error will occur.
The annotator requires two external resources:
GateApplication The .gapp file containing the saved application state.
MappingDescriptor The mapping descriptor XML file.
These must be bound to suitable URLs, either by editing the resourceManagerConfiguration
section of the primitive descriptor, or by supplying the binding in an aggregate descriptor
that includes the GATEApplicationAnnotator as one of its delegates.
In addition, you may need to set the following Java system properties:
uima.gate.configdir The path to the GATE config directory.
This defaults to
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435
gate-config in the same directory as uima-gate.jar.
uima.gate.siteconfig The location of the sitewide gate.xml configuration file. This defaults to gate.uima.configdir /site-gate.xml.
uima.gate.userconfig The location of the user-specific gate.xml configuration file. This
defaults to gate.uima.configdir /user-gate.xml.
The default config files are deliberately simplified from the standard versions supplied with
GATE, in particular they do not load any plugins automatically (not even ANNIE). All
the plugins used by your application are specified in the .gapp file, and will be loaded
when the application is loaded, so it is best to avoid loading any others from gate.xml, to
avoid problems such as two different versions of the same plugin being loaded from different
locations.
Classpath Notes
In addition to the usual UIMA library JAR files, GATEApplicationAnnotator requires a
number of JAR files from the GATE distribution in order to function. In the first instance, you should include gate.jar from GATE’s bin directory, and also all the JAR files
from GATE’s lib directory on the classpath. If you use the supplied Ant build file, ant
documentanalyser will run the document analyser with this classpath. Depending on exactly which GATE plugins your application uses, you may be able to exclude some of the
lib JAR files (for example, you will not need Weka if you do not use the machine learning
plugin), but it is safest to start with them all. GATE will load plugin JAR files through its
own classloader, so these do not need to be on the classpath.
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Chapter 20
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For the previous reader was none other than myself. I had already read this book
long ago.
The old sickness has me in its grip again: amnesia in litteris, the total loss of
literary memory. I am overcome by a wave of resignation at the vanity of all
striving for knowledge, all striving of any kind. Why read at all? Why read
this book a second time, since I know that very soon not even a shadow of
a recollection will remain of it? Why do anything at all, when all things fall
apart? Why live, when one must die? And I clap the lovely book shut, stand
up, and slink back, vanquished, demolished, to place it again among the mass of
anonymous and forgotten volumes lined up on the shelf.
...
But perhaps - I think, to console myself - perhaps reading (like life) is not a
matter of being shunted on to some track or abruptly off it. Maybe reading is
an act by which consciousness is changed in such an imperceptible manner that
the reader is not even aware of it. The reader suffering from amnesia in litteris
is most definitely changed by his reading, but without noticing it, because as
he reads, those critical faculties of his brain that could tell him that change
is occurring are changing as well. And for one who is himself a writer, the
sickness may conceivably be a blessing, indeed a necessary precondition, since
it protects him against that crippling awe which every great work of literature
creates, and because it allows him to sustain a wholly uncomplicated relationship
to plagiarism, without which nothing original can be created.
Three Stories and a Reflection, Patrick Suskind, 1995 (pp. 82, 86).
This chapter describes additional CREOLE resources which do not form part of ANNIE,
and have not been covered in previous chapters.
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Verb Group Chunker
The rule-based verb chunker is based on a number of grammars of English [Cobuild 99,
Azar 89]. We have developed 68 rules for the identification of non recursive verb groups.
The rules cover finite (’is investigating’), non-finite (’to investigate’), participles (’investigated’), and special verb constructs (’is going to investigate’). All the forms may include
adverbials and negatives. The rules have been implemented in JAPE. The finite state analyser produces an annotation of type ‘VG’ with features and values that encode syntactic
information (‘type’, ‘tense’, ‘voice’, ‘neg’, etc.). The rules use the output of the POS tagger
as well as information about the identity of the tokens (e.g. the token ‘might’ is used to
identify modals).
The grammar for verb group identification can be loaded as a Jape grammar into the GATE
architecture and can be used in any application: the module is domain independent.
20.2
Noun Phrase Chunker
The NP Chunker application is a Java implementation of the Ramshaw and Marcus BaseNP
chunker (in fact the files in the resources directory are taken straight from their original
distribution) which attempts to insert brackets marking noun phrases in text which have
been marked with POS tags in the same format as the output of Eric Brill’s transformational
tagger. The output from this version should be identical to the output of the original
C++/Perl version released by Ramshaw and Marcus.
For more information about baseNP structures and the use of transformation-based learning
to derive them, see [Ramshaw & Marcus 95].
20.2.1
Differences from the Original
The major difference is the assumption is made that if a POS tag is not in the mapping
file then it is tagged as ‘I’. The original version simply failed if an unknown POS tag was
encountered. When using the GATE wrapper the chunk tag can be changed from ‘I’ to any
other legal tag (B or O) by setting the unknownTag parameter.
20.2.2
Using the Chunker
The Chunker requires the Creole plugin ‘Parser NP Chunking’ to be loaded. The two loadtime parameters are simply urls pointing at the POS tag dictionary and the rules file, which
should be set automatically. There are five runtime parameters which should be set prior to
executing the chunker.
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• annotationName: name of the annotation the chunker should create to identify noun
phrases in the text.
• inputASName: The chunker requires certain types of annotations (e.g. Tokens with
part of speech tags) for identifying noun chunks. This parameter tells the chunker
which annotation set to use to obtain such annotations from.
• outputASName: This is where the results (i.e. new noun chunk annotations will be
stored).
• posFeature: Name of the feature that holds POS tag information. ’
• unknownTag: it works as specified in the previous section.
The chunker requires the following PRs to have been run first: tokeniser, sentence splitter,
POS tagger.
20.3
TaggerFramework
The Tagger Framework is an extension of work originally developed in order to provide support for the TreeTagger plugin within GATE. Rather than focusing on providing support for
a single external tagger this plugin provides a generic wrapper that can easily be customised
(no Java code is required) to incorporate many different taggers within GATE.
The plugin currently provides example applications (see plugins/Tagger Framework/resources)
for the following taggers: GENIA (a biomedical tagger), Hunpos (providing support for English and Hungarian), TreeTagger (supporting German, French, Spanish and Italian as well
as English), and the Stanford Tagger (supporting English, German and Arabic).
The basic idea behind this plugin is to allow the use of many external taggers. Providing
such a generic wrapper requires a few assumptions. Firstly we assume that the external
tagger will read from a file and that the contents of this file will be one annotation per line
(i.e. one token or sentence per line). Secondly we assume that the tagger will write it’s
response to stdout and that it will also be based on one annotation per line – although there
is no assumption that the input and output annotation types are the same.
An important issue with most external taggers is tokenisation: Generally, when using a
native GATE tagger in a pipeline, “Token” annotations are first generated by a tokeniser,
and then processed by a POS tagger. Most external taggers, on the other hand, have built-in
code to perform their own tokenisation. In this case, there are generally two options: (1) use
the tokens generated by the external tagger and import them back into GATE (typically
into a “Token” annotation type). Or (2), if the tagger accepts pre-tokenised text, the Tagger
Framework can be configured to pass the annotations as generated by a GATE tokeniser
to the external tagger. For details on this, please refer to the ‘updateAnnotations’ runtime
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parameter described below. However, if the tokenisation strategies are significantly different,
this may lead to a degradation of the tagger’s performance.
• Initialization Parameters
– preProcessURL: The URL of a JAPE grammar that should be run over each
document before running the tagger.
– postProcessURL: The URL of a JAPE grammar that should be run over each
document after running the tagger. This can be used, for example, to add chunk
annotations using IOB tags output by the tagger and stored as features on Token
annotations.
• Runtime Parameters
– debug: if set to true then a whole heap of useful information will be printed to
the messages tab as the tagger runs. Defaults to false.
– encoding: this must be set to the encoding that the tagger expects the input/output files to use. If this is incorrectly set is highly likely that either the tagger will
fail or the results will be meaningless. Defaults to ISO-8859-1 as this seems to
be the most commonly required encoding.
– failOnUnmappableCharacter: What to do if a character is encountered in the
document which cannot be represented in the selected encoding. If the parameter is true (the default), unmappable characters cause the wrapper to throw
an exception and fail. If set to false, unmappable characters are replaced by
question marks when the document is passed to the tagger. This is useful if your
documents are largely OK but contain the odd character from outside the Latin-1
range.
– inputTemplate: template string describing how to build the line of input for the
tagger corresponding to a single annotation. The template contains placeholders
of the form ${feature} which will be replaced by the value of the corresponding
feature from the annotation. The default template is ${string}, which simply
passes the string feature of each annotation to the tagger. Typical variants would
be ${string}\t${category} for an entity tagger that requires the string and the
part of speech tag for each token, separated by a tab1 . If a particular annotation
does not have one of the specified features, the corresponding slot in the template
will be left blank (i.e. replaced by an empty string). It is only an error if a
particular annotation contains none of the features specified by the template.
– regex: this should be a Java regular expression that matches a single line in the
output from the tagger. Capturing groups should be used to define the sections
of the expression which match the useful output.
1
Java string escape sequences such as \t will be decoded before the template is expanded.
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– featureMapping: this is a mapping from feature name to capturing group in the
regular expression. Each feature will be added to the output annotations with a
value equal to the specified capturing group. For example, the TreeTagger uses a
regular expression (.+)\t(.+)\t(.+) to capture the three column output. This
is then combined with the feature mapping {string=1, category=2, lemma=3}
to add the appropriate feature/values to the output annotations.
– inputASName: the name of the annotation set which should be used for input.
If not specified the default (i.e. un-named) annotation set will be used.
– inputAnnotationType: the name of the annotation used as input to the tagger.
This will usually be Token. Note that the input annotations must contain a
string feature which will be used as input to the tagger. Tokens usually have
this feature but if, for example, you wish to use Sentence as the input annotation
then you will need to add the string feature. JAPE grammars for doing this are
provided in plugins/Tagger Framework/resources.
– outputASName: the name of the annotation set which should be used for
output. If not specified the default (i.e. un-named) annotation set will be used.
– outputAnnotationType: the name of the annotation to be provided as output.
This is usually Token.
– taggerBinary: a URL indicating the location of the external tagger. This is usually a shell script which may perform extra processing before executing the tagger. The plugins/Tagger Framework/resources directory contains example scripts
(where needed) for the supported taggers. These scripts may need editing (for
example, to set the installation directory of the tagger) before they can be used.
– taggerDir: the directory from which the tagger must be executed. This can be
left unspecified.
– taggerFlags: an ordered set of flags that should be passed to the tagger as
command line options
– updateAnnotations: If set to true then the plugin will attempt to update existing output annotations. This can fail if the output from the tagger and the
existing annotations are created differently (i.e. the tagger does its own tokenization). Setting this option to false will make the plugin create new output annotations, removing any existing ones, to prevent the two sets getting out of sync.
This is also useful when the tagger is domain specific and may do a better job
than GATE. For example, the GENIA tagger is better at tokenising biomedical
text than the ANNIE tokeniser. Defaults to true.
By default the GenericTagger PR simply tries to execute the taggerBinary using the normal
Java Runtime.exec() mechanism. This works fine on Unix-style platforms such as Linux or
Mac OS X, but on Windows it will only work if the taggerBinary is a .exe file. Attempting
to invoke other types of program fails on Windows with a rather cryptic “error=193”.
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To support other types of tagger programs such as shell scripts or Perl scripts, the GenericTagger PR supports a Java system property shell.path. If this property is set then
instead of invoking the taggerBinary directly the PR will invoke the program specified by
shell.path and pass the tagger binary as the first command-line parameter.
If the tagger program is a shell script then you will need to install the appropriate interpreter, such as sh.exe from the cygwin tools, and set the shell.path system property to
point to sh.exe. For GATE Developer you can do this by adding the following line to
build.properties (see Section 2.3, and note the extra backslash before each backslash and
colon in the path):
run.shell.path: C\:\\cygwin\\bin\\sh.exe
Similarly, for Perl or Python scripts you should install a suitable interpreter and set
shell.path to point to that.
You can also run taggers that are invoked using a Windows batch file (.bat). To use
a batch file you do not need to use the shell.path system property, but instead set
the taggerBinary runtime parameter to point to C:\WINDOWS\system32\cmd.exe and
set the first two taggerFlags entries to “/c” and the Windows-style path to the tagger batch file (e.g. C:\MyTagger\runTagger.bat). This will cause the PR to run
cmd.exe /c runTagger.bat which is the way to run batch files from Java.
20.3.1
TreeTagger - Multilingual POS Tagger
The TreeTagger is a language-independent part-of-speech tagger, which supports a number
of different languages through parameter files, including English, French, German, Spanish,
Italian and Bulgarian. Originally made available in GATE through a dedicated wrapper,
it is now fully supported through the Tagger Framework. You must install the TreeTagger
separately from
http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger/DecisionTreeTagger.html
Avoid installing it in a directory that contains spaces in its path.
Tokenisation and Command Scripts. When running the TreeTagger through the Tagger Framework, you can choose between passing Tokens generated within GATE to the
TreeTagger for POS tagging or let the TreeTagger perform tokenisation as well, importing
the generated Tokens into GATE annotations. If you need to pass the Tokens generated
by GATE to the TreeTagger, it is important that you create your own command scripts to
skip the tokenisation step done by default in the TreeTagger command scripts (the ones in
the TreeTagger’s cmd directory). A few example scripts for passing GATE Tokens to the
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TreeTagger are available under plugins/Tagger_Framework/resources/TreeTagger, for
example, tree-tagger-german-gate runs the German parameter file with existing “Token”
annotations.
Note that you must set the paths in these command files to point to the location where you
installed the TreeTagger:
BIN=/usr/local/durmtools/TreeTagger/bin
CMD=/usr/local/durmtools/TreeTagger/cmd
LIB=/usr/local/durmtools/TreeTagger/lib
The Tagger Framework will run the TreeTagger on any platform that supports the TreeTagger tool, including Linux, Mac OS X and Windows, but the GATE-specific scripts
require a POSIX-style Bourne shell with the gawk, tr and grep commands, plus Perl
for the Spanish tagger. For Windows this means that you will need to install the appropriate parts of the Cygwin environment from http://www.cygwin.com and set the
system property treetagger.sh.path to contain the path to your sh.exe (typically
C:\cygwin\bin\sh.exe).
POS Tags. For English the POS tagset is a slightly modified version of the Penn Treebank
tagset, where the second letter of the tags for verbs distinguishes between ‘be’ verbs (B),
‘have’ verbs (H) and other verbs (V).
Figure 20.1: A French document processed by the TreeTagger through the Tagger Framework
The tagsets for other languages can be found on the TreeTagger web site. Figure 20.1 shows
a screenshot of a French document processed with the TreeTagger.
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20.4
Chemistry Tagger
This GATE module is designed to tag a number of chemistry items in running text. Currently
the tagger tags compound formulas (e.g. SO2, H2O, H2SO4 ...) ions (e.g. Fe3+, Cl-) and
element names and symbols (e.g. Sodium and Na). Limited support for compound names
is also provided (e.g. sulphur dioxide) but only when followed by a compound formula (in
parenthesis or commas).
20.4.1
Using the Tagger
The Tagger requires the Creole plugin ‘Tagger Chemistry’ to be loaded. It requires the
following PRs to have been run first: tokeniser and sentence splitter (the annotation set containing the Tokens and Sentences can be set using the annotationSetName runtime parameter). There are four init parameters giving the locations of the two gazetteer list definitions,
the element mapping file and the JAPE grammar used by the tagger (in previous versions
of the tagger these files were fixed and loaded from inside the ChemTagger.jar file). Unless
you know what you are doing you should accept the default values.
The annotations added to documents are ‘ChemicalCompound’, ‘ChemicalIon’ and ‘ChemicalElement’ (currently they are always placed in the default annotation set). By default
‘ChemicalElement’ annotations are removed if they make up part of a larger compound or
ion annotation. This behaviour can be changed by setting the removeElements parameter
to false so that all recognised chemical elements are annotated.
20.5
Annotating Numbers
The Tagger Numbers creole repository contains a number of processing resources which are
designed to annotate numbers appearing within documents. As well as annotating a given
span as being a number the PRs also determine the exact numeric value of the number and
add this as a feature of the annotation. This makes the annotations created by these PRs
ideal for building more complex annotations such as measurements or monetary units.
All the PRs in this plugin produce Number annotations with the following standard features
• type: this describes the types of tokens that make up the number, e.g. roman, words,
numbers
• value: this is the actual value (stored as a Double) of the number that has been
annotated
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String
Value
3ˆ2
9
101
101
3,000
3000
3.3e3
3300
1/4
0.25
9ˆ1/2
3
4x10ˆ3
4000
5.5*4ˆ5
5632
thirty one
31
three hundred
300
four thousand one hundred and two 4102
3 million
3000000
fünfundzwanzig
25
Table 20.1: Numbers Tagger Examples
Each PR might also create other features which are described, along with the PR, in the
following sections.
20.5.1
Numbers in Words and Numbers
The “Numbers Tagger” annotates numbers made up from numbers or numeric words. If that
wasn’t really clear enough then Table 20.1 shows numerous ways of representing numbers
that can all be annotated by this tagger (depending upon the configuration files used).
To create an instance of the PR you will need to configure the following initialization time
parameters (sensible defaults are provided):
• configURL: the URL of the configuration file you wish to use (see below for details),
defaults to resources/languages/all.xml which currently provides support for English, French, German, Spanish and a variety of number related Unicode symbols.
If you want a single language the you can specify the appropriately named file, i.e.
resources/languages/english.xml.
• encoding: the encoding of the configuration file, defaults to UTF-8
• postProcessURL: the URL of the JAPE grammar used for post-processing – don’t
change this unless you know what you are doing!
The configuration file is an XML document that specifies the words that can be used as
numbers or multipliers (such as hundred, thousand, ...) and conjunctions that can then be
used to combine sequences of numbers together. An example configuration file can be seen in
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<config>
<description>Basic Example</description>
<imports>
<url encoding="UTF-8">symbols.xml</url>
</imports>
<words>
<word value="0">zero</word>
<word value="1">one</word>
<word value="2">two</word>
<word value="3">three</word>
<word value="4">four</word>
<word value="5">five</word>
<word value="6">six</word>
<word value="7">seven</word>
<word value="8">eight</word>
<word value="9">nine</word>
<word value="10">ten</word>
</words>
<multipliers>
<word value="2">hundred</word>
<word value="2">hundreds</word>
<word value="3">thousand</word>
<word value="3">thousands</word>
</multipliers>
<conjunctions>
<word whole="true">and</word>
</conjunctions>
<decimalSymbol>.</decimalSymbol>
<digitGroupingSymbol>,</digitGroupingSymbol>
</config>
Figure 20.2: Example Numbers Tagger Config File
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Figure 20.2. This configuration file specifies a handful of words and multipliers and a single
conjunction. It also imports another configuration file (in the same format) defining Unicode
symbols. Most of the configuration file is self-explanatory, however, the conjunctions needs
further clarification. In English conjunctions are whole words, that is they require white
space on either side of them, e.g. three hundred and one. In other languages, however,
numbers can be joined into a single word using a conjunction. For example, in German the
conjunction ‘und’ can appear in a number without white space, e.g. twenty one is written as
einundzwanzig. If the conjunction is a whole word, as in English, then the whole attribute
should be set to true, but for conjunctions like ‘und’ the attribute should be set to false.
In order to support different number formats the symbols used to group numbers and to
represent the decimal point can also be configured. These are optional elements in the XML
configuration file which if not supplied default to a comma for the digit group symbol and
a full stop for the decimal point. Whilst these are appropriate for many languages if you
wanted, for example, to parse documents written in Bulgarian you would want to specify
that the decimal symbol was a command and the grouping symbol was a space in order to
recognise numbers such as 1 000 000,303.
Once created an instance of the PR can then be configured using the following runtime
parameters:
• allowWithinWords: digits can often occur within words (for example part numbers,
chemical equations etc.) where they should not be interpreted as numbers. If this
parameter is set to true then these instances will also be annotated as numbers (useful
for annotating money and measurements where spaces are often omitted), however,
the parameter defaults to false.
• annotationSetName: the annotation set to use as both input and output for this
PR (due to the way this PR works the two sets have to be the same)
• failOnMissingInputAnnotations: if the input annotations (Tokens and Sentences)
are missing should this PR fail or just not do anything, defaults to true to allow obvious
mistakes in pipeline configuration to be captured at an early stage.
• useHintsFromOriginalMarkups: often the original markups will provide hints that
may be useful for correctly interpreting numbers within documents (i.e. numeric powers
may be in <sup></sup> tags), if this parameter is set to true then these hints will
be used to help parse the numbers, defaults to true.
There are no extra annotation features which are specific to this numbers PR. The type
feature can take one of three values based upon the text that is annotated; words, numbers,
wordsAndNumbers.
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20.5.2
Roman Numerals
The “Roman Numerals Tagger” annotates Roman numerals appearing in the document. The
tagger is configured using the following runtime parameters:
• allowLowerCase: traditionally Roman numerals must be all in uppercase. Setting
this parameter to false, however, allows Roman numerals written in lowercase to also
be annotated. This parameter defaults to false.
• maxTailLength: Roman numerals are often used in labelling sections, figures, tables
etc. and in such cases can be followed by additional information. For example, Table
IVa, Appendix IIIb. These characters are referred to as the tail of the number and this
parameter constrains the number of characters that can appear. The default value is
0 in which case strings such as ’IVa’ would not be annotated in any way.
• outputASName: the name of the annotation set in which the Number annotations
should be created.
As well as the normal Number annotation features (the type feature will always take the
value ‘roman’ Roman numeral annotations also include the following features:
• tail: contains the tail, if any, that appears after the Roman numeral.
20.6
Annotating Measurements
Measurements mentioned in text documents can be difficult to accurately deal with. As well
as the numerous ways in which numeric values can be written each type of measurement
(distance, area, time etc.) can be written using a variety of different units. For example,
lengths can be measured in metres, centimetres, inches, yards, miles, furlongs and chains, to
mention just a few. Whilst measurements may all have different units and values they can, in
theory be compared to one another. Extracting, normalizing and comparing measurements
can be a useful IE process in many different domains. The Measurement Tagger (which can
be found in the Tagger Measurements plugin) attempts to provide such annotations for use
within IE applications.
The Measurements Tagger uses a parser based upon a modified version of the Java port
of the GNU Units package. This allows us to not only recognise and annotation spans of
text as being a measurement but also to normalize the units to allow for easy comparison of
different measurement values.
This PR actually produces two different annotations; Measurement and Ratio.
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Measurement annotations represent measurements that involve a unit, e.g. 3mph, three
pints, 4 m3 . Single measurements (i.e. those not referring to a range or interval) are referred
to as scalar measurements and have the following features:
• type: for scalar measurements is always scalar
• unit: the unit as recognised from the text. Note that this won’t necessarily be the
annotated text. For example, an annotation spanning the text “three miles” would
have a unit feature of “mile”.
• value: a Double holding the value of the measurement (this usually comes directly
from the value feature of a Number annotation).
• dimension: the measurements dimension, e.g. speed, volume, area, length, time etc.
• normalizedUnit: to enable measurements of the same dimension but specified in
different units to be compared the PR reduces all units to their base form. A base
form usually consists of a combination of SI units. For example, centimetre, mm, and
kilometre are all normalized to m (for metre).
• normalizedValue: a Double instance holding the normalized value, such that the combination of the normalized value and normalized unit represent the same measurement
as the original value and unit.
• normalized: a String representing the normalized measurement (usually a simple space
separated concatenation of the normalized value and unit).
Annotations which represent an interval or range have a slightly different set of features.
The type feature is set to interval, there is no normalized or unit feature and the value
features (included the normalized version) are replaced by the following features, the values
of which are simply copied from the Measurement annotations which mark the boundaries
of the interval.
• normalizedMinValue: a Double representing the minimum normalized number that
forms part of the interval.
• normalizedMaxValue: a Double representing the minimum normalized number that
forms part of the interval.
Interval annotations do not replace scalar measurements and so multiple Measurement annotations may well overlap. They can of course be distinguished by the type feature.
As well as Measurement annotations the tagger also adds Ratio annotations to documents.
Ratio annotations cover measurements that do not have a unit. Percentages are the most
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common ratios to be found in documents, but also amounts such as “300 parts per million”
are annotated.
A Ratio annotation has the following features:
• value: a Double holding the actual value of the ratio. For example, 20% will have a
value of 0.2.
• numerator: the numerator of the ratio. For example, 20% will have a numerator of
20.
• denominator: the denominator of the ratio. For example, 20% will have a denominator
of 100.
An instance of the measurements tagger is created using the following initialization parameters:
• commonURL: this file defines units that are also common words and so should not
be annotated as a measurement unless they form a compound unit involving two or
more unit symbols. For example, C is the accepted abbreviation for coulomb but often
appears in documents as part of a reference to a table or figure, i.e. Figure 3C, which
should not be annotated as a measurement. The default file was hand tuned over a
large patent corpus but may need to be edited when used with different domains.
• encoding: the encoding to use when reading both of the configuration files, defaults
to UTF-8.
• japeURL: the URL of the JAPE grammar that drives the measurement parser. Unless
you really know what you are doing, the value of this parameter should not be changed.
• locale: the locale to use when parsing the units definition file, defaults to en GB.
• unitsURL: the URL of the main unit definition file to use. This should be in the
same format as accepted by the GNU Units package.
The PR does not attempt to recognise or annotate numbers, instead it relies on Number
annotations being present in the document. Whilst these annotations could be generated by
any resource executed prior to the measurements tagger, we recommend using the Numbers
Tagger described in Section 20.5. If you choose to produce Number annotations in some
other way note that they must have a value feature containing a Double representing the
value of the number. An example GATE application, showing how to configure and use the
two PRs together, is provided with the measurements plugin.
Once created an instance of the tagger can be configured using the following runtime parameters:
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• consumeNumberAnnotations: if true then Number annotations used to find measurements will be consumed and removed from the document, defaults to true.
• failOnMissingInputAnnotations: if the input annotations (Tokens) are missing
should this PR fail or just not do anything, defaults to true to allow obvious mistakes
in pipeline configuration to be captured at an early stage.
• ignoredAnnotations: a list of annotation types in which a measurement can never
occur, defaults to a set containing Date and Money.
• inputASName: the annotation set used as input to this PR.
• outputASName: the annotation set to which new annotations will be added.
The ability to prevent the tagger from annotating measurements which occur within other
annotations is a very useful feature. The runtime parameters, however, only allow you to
specify the names of annotations and not to restrict on feature values or any other information
you may know about the documents being processed. Internally ignoring sections of a
document is controlled by adding CannotBeAMeasurement annotations that span the text
to be ignored. If you need greater control over the process than the ignoredAnnotations
parameter allows then you can create CannotBeAMeasurement annotations prior to running
the measurement tagger, for example a JAPE grammar placed before the tagger in the
pipeline. Note that these annotations will be deleted by the measurements tagger once
processing has completed.
20.7
Annotating and Normalizing Dates
Many information extraction tasks benefit from or require the extraction of accurate date
information. While ANNIE (Chapter 6) does produce Date annotations no attempt is made
to normalize these dates, i.e. to firmly fix all dates, even partial or relative ones, to a timeline
using a common date representation. The PR in the Tagger DateNormalizer plugin attempts
to fill this gap by normalizing dates against the date of the document (see below for details
on how this is determined) in order to tie each Date annotation to a specific date. This
includes normalizing dates such as April 1st, today, yesterday, and next Tuesday, as well as
converting fully specified dates (ones in which the day, month and year are specified) into a
common format.
Different cultures/countries have different conventions for writing dates, as well as different
languages using different words for the days of the week and the months of the year. The
parser underlying this PR makes use of the locale-specific information when parsing documents. When initializing an instance of the Date Normalizer you can specify the locale to
use using ISO language and country codes along with Java specific variants (for details of
these codes see the Java Locale documentation). So for example, to specify Britsh English
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(which means the day usually comes before the month in a date) use en GB, or for American
English (where the month usually appears before the day in a date) specify en US. If you
need to override the locale on a document basis then you can do this by setting a document
feature called locale to a string encoded as above. If neither the initialization parameter or
document feature are present or do not represent a valid locale then the default locale of the
JVM running GATE will be used.
Once initialized and added to a pipeline the Date Normalizer has the following runtime
parameters that can be used to control it’s behaviour.
• dateFormat: the format that dates should be normalized to. The format of this
parameter is the same as that use by the Java SimpleDateFormat whose documentation
describes the full range of possible formats. This defaults to dd/MM/yyyy. Note that
this parameter is only required if the numericOuput parameter is set to false.
• failOnMissingInputAnnotations: if the input annotations (Tokens) are missing
should this PR fail or just not do anything, defaults to true to allow obvious mistakes
in pipeline configuration to be captured at an early stage.
• inputASName: the annotation set used as input to this PR.
• normalizedDocumentFeature: if set then the normalized version of the document
date will be stored in a document feature with this name. This parameter defaults
to normalized-date although it can be left blank to suppress storage of the document
date.
• numericOutput: if true then instead of formatting the normalized dates as String
features of the Date annotations they are instead converted into a numeric representation. Specifically the first converted to the form yyyyMMdd and then cast to a Double.
This is useful as dates can then be sorted numerical (which is fast) into order. If false
then the formatting string in the dateFormat parameter is used instead to create a
string representation. This defaults to false.
• outputASName: the annotation set to which new annotations will be added.
• sourceOfDocumentDate: this parameter is a list of the names of annotations, annotation features (encoded as Annotation.feature), and document features to inspect
when trying to determine the date of the document. The PR works through the list
getting the text of feature or under the annotation (if no feature is specified) and then
parsing this to find a fully specified date, i.e. one where the day, month and year are
all present. Once a date is found processing of the list stops and the date is used as the
date of the document. If you specify an annotation that can occur multiple times in a
document then they are sorted based on a numeric priority feature (which defaults to
0) or their order within the document. The idea here is that there are multiple ways
in which to determine the date of a document but most are domain specific and this
allows previous PRs in an application to determine the document date. This defaults
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to an empty list which is taken to assume that the document was written on the day
it is being processed. The same assumption applies if no fully-specified date can be
found once the whole list has been processed.
It is important to note that rather this plugin creates new Date annotations and so if you run
it in the same pipeline as the ANNIE NE Transducer you will likely end up with overlapping
Date annotations. Depending on your needs it may be that you need a JAPE grammar to
delete ANNIE Date annotations before running this PR. In practice we have found that the
Date annotations added by ANNIE can be a good source of document dates and so a JAPE
grammar that uses ANNIE Dates to add new DocumentDate annotations and to delete other
Date annotations can be a useful step before running this PR.
20.8
ABNER - A Biomedical Named Entity Recogniser
ABNER is A Biomedical Named Entity Recogniser. It uses machine learning (linear-chain
conditional random fields, CRFs) to find entities such as genes, cell types, and DNA in text.
Full details of ABNER can be found at http://pages.cs.wisc.edu/ bsettles/abner/
The ABNER plugin, called ‘Tagger Abner’, contains a single PR, called AbnerTagger, which
wraps ABNER. To use AbnerTagger, first load the Tagger Abner plugin through the plugins
console, and then create a new AbnerTagger PR in the usual way. The AbnerTagger PR has
no loadtime parameters (apart from Name). It does not require any other PRs to be run
prior to execution.
The AbnerTagger has two runtime parameters:
• abnerMode The Abner model that will be used for tagging. The plugin can use one
of two previously trained machine learning models for tagging text, as provided by
Abner:
– BIOCREATIVE trained on the BioCreative corpus
– NLPBA trained on the NLPBA corpus
• outputASName The name of the output annotation set to which AbnerTagger output
will be written.
The AbnerTagger creates annotations of type ‘Tagger’ with a feature and value
‘source=abner’. Each annotation may also have features of ‘class’ and ‘type’ set by Abner to values such as:
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• Protein
• DNA
• RNA
• Cell Line
• Cell Type
• Gene
Abner does support training of models on other data, but this functionality is not, however,
supported by the GATE wrapper.
For
further
details
please
refer
http://pages.cs.wisc.edu/ bsettles/abner/
20.9
to
the
Abner
documentation
at
Snowball Based Stemmers
The stemmer plugin, ‘Stemmer Snowball’, consists of a set of stemmers PRs for the following 11 European languages: Danish, Dutch, English, Finnish, French, German, Italian,
Norwegian, Portuguese, Russian, Spanish and Swedish. These take the form of wrappers
for the Snowball stemmers freely available from http://snowball.tartarus.org. Each Token is
annotated with a new feature ‘stem’, with the stem for that word as its value. The stemmers
should be run as other PRs, on a document that has been tokenised.
There are three runtime parameters which should be set prior to executing the stemmer on
a document.
• annotationType: This is the type of annotations that represent tokens in the document.
Default value is set to ‘Token’.
• annotationFeature: This is the name of a feature that contains tokens’ strings. The
stemmer uses value of this feature as a string to be stemmed. Default value is set to
‘string’.
• annotationSetName: This is where the stemmer expects the annotations of type as
specified in the annotationType parameter to be.
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Algorithms
The stemmers are based on the Porter stemmer for English [Porter 80], with rules implemented in Snowball e.g.
define Step_1a as
( [substring] among (
’sses’ (<-’ss’)
’ies’ (<-’i’)
’ss’ () ’s’ (delete)
)
20.10
GATE Morphological Analyzer
The Morphological Analyser PR can be found in the Tools plugin. It takes as input a
tokenized GATE document. Considering one token and its part of speech tag, one at a
time, it identifies its lemma and an affix. These values are than added as features on the
Token annotation. Morpher is based on certain regular expression rules. These rules were
originally implemented by Kevin Humphreys in GATE1 in a programming language called
Flex. Morpher has a capability to interpret these rules with an extension of allowing users
to add new rules or modify the existing ones based on their requirements. In order to allow
these operations with as little effort as possible, we changed the way these rules are written.
More information on how to write these rules is explained later in Section 20.10.1.
Two types of parameters, Init-time and run-time, are required to instantiate and execute
the PR.
• rulesFile (Init-time) The rule file has several regular expression patterns. Each pattern
has two parts, L.H.S. and R.H.S. L.H.S. defines the regular expression and R.H.S. the
function name to be called when the pattern matches with the word under consideration. Please see 20.10.1 for more information on rule file.
• caseSensitive (init-time) By default, all tokens under consideration are converted into
lowercase to identify their lemma and affix. If the user selects caseSensitive to be true,
words are no longer converted into lowercase.
• document (run-time) Here the document must be an instance of a GATE document.
• affixFeatureName (run-time) Name of the feature that should hold the affix value.
• rootFeatureName (run-time) Name of the feature that should hold the root value.
• annotationSetName (run-time) Name of the annotationSet that contains Tokens.
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• considerPOSTag (run-time) Each rule in the rule file has a separate tag, which specifies
which rule to consider with what part-of-speech tag. If this option is set to false, all
rules are considered and matched with all words. This option is very useful. For
example if the word under consideration is ”singing”. ”singing” can be used as a noun
as well as a verb. In the case where it is identified as a verb, the lemma of the same
would be ”sing” and the affix ”ing”, but otherwise there would not be any affix.
• failOnMissingInputAnnotations (run-time) If set to true (the default) the PR will terminate with an Exception if none of the required input Annotations are found in a
document. If set to false the PR will not terminate and instead log a single warning
message per session and a debug message per document that has no input annotations.
20.10.1
Rule File
GATE provides a default rule file, called default.rul, which is available under the gate/plugins/Tools/morph/resources directory. The rule file has two sections.
1. Variables
2. Rules
Variables
The user can define various types of variables under the section defineVars. These variables
can be used as part of the regular expressions in rules. There are three types of variables:
1. Range With this type of variable, the user can specify the range of characters. e.g. A
==> [-a-z0-9]
2. Set With this type of variable, user can also specify a set of characters, where one
character at a time from this set is used as a value for the given variable. When this
variable is used in any regular expression, all values are tried one by one to generate the string which is compared with the contents of the document. e.g. A ==>
[abcdqurs09123]
3. Strings Where in the two types explained above, variables can hold only one character
from the given set or range at a time, this allows specifying strings as possibilities for
the variable. e.g. A ==> ‘bb’ OR ‘cc’ OR ‘dd’
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Rules
All rules are declared under the section defineRules. Every rule has two parts, LHS and
RHS. The LHS specifies the regular expression and the RHS the function to be called when
the LHS matches with the given word. ‘==>’ is used as delimiter between the LHS and
RHS.
The LHS has the following syntax:
< ” ∗ ”—”verb”—”noun” >< regularexpression >.
User can specify which rule to be considered when the word is identified as ‘verb’ or ‘noun’.
‘*’ indicates that the rule should be considered for all part-of-speech tags. If the part-ofspeech should be used to decide if the rule should be considered or not can be enabled or
disabled by setting the value of considerPOSTags option. Combination of any string along
with any of the variables declared under the defineVars section and also the Kleene operators,
‘+’ and ‘*’, can be used to generate the regular expressions. Below we give few examples of
L.H.S. expressions.
• <verb>”bias”
• <verb>”canvas”{ESEDING} ”ESEDING” is a variable defined under the defineVars
section. Note: variables are enclosed with ”{” and ”}”.
• <noun>({A}*”metre”) ”A” is a variable followed by the Kleene operator ”*”, which
means ”A” can occur zero or more times.
• <noun>({A}+”itis”) ”A” is a variable followed by the Kleene operator ”+”, which
means ”A” can occur one or more times.
• < ∗ >”aches” ”< ∗ >” indicates that the rule should be considered for all part-ofspeech tags.
On the RHS of the rule, the user has to specify one of the functions from those listed
below. These rules are hard-coded in the Morph PR in GATE and are invoked if the regular
expression on the LHS matches with any particular word.
• stem(n, string, affix ) Here,
– n = number of characters to be truncated from the end of the string.
– string = the string that should be concatenated after the word to produce the
root.
– affix = affix of the word
• irreg stem(root, affix ) Here,
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– root = root of the word
– affix = affix of the word
– null stem() This means words are themselves the base forms and should not be
analyzed.
• semi reg stem(n,string) semir reg stem function is used with the regular expressions
that end with any of the {EDING} or {ESEDING} variables defined under the variable
section. If the regular expression matches with the given word, this function is invoked,
which returns the value of variable (i.e. {EDING} or {ESEDING}) as an affix. To
find a lemma of the word, it removes the n characters from the back of the word and
adds the string at the end of the word.
20.11
Flexible Exporter
The Flexible Exporter enables the user to save a document (or corpus) in its original format
with added annotations. The user can select the name of the annotation set from which these
annotations are to be found, which annotations from this set are to be included, whether
features are to be included, and various renaming options such as renaming the annotations
and the file.
At load time, the following parameters can be set for the flexible exporter:
• includeFeatures - if set to true, features are included with the annotations exported; if
false (the default status), they are not.
• useSuffixForDumpFiles - if set to true (the default status), the output files have the
suffix defined in suffixForDumpFiles; if false, no suffix is defined, and the output file
simply overwrites the existing file (but see the outputFileUrl runtime parameter for an
alternative).
• suffixForDumpFiles - this defines the suffix if useSuffixForDumpFiles is set to true. By
default the suffix is .gate.
• useStandOffXML - if true then the format will be the GATE XML format that separates nodes and annotations inside the file which allows overlapping annotations to be
saved.
The following runtime parameters can also be set (after the file has been selected for the
application):
• annotationSetName - this enables the user to specify the name of the annotation set
which contains the annotations to be exported. If no annotation set is defined, it will
use the Default annotation set.
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• annotationTypes - this contains a list of the annotations to be exported. By default it
is set to Person, Location and Date.
• dumpTypes - this contains a list of names for the exported annotations. If the annotation name is to remain the same, this list should be identical to the list in annotationTypes. The list of annotation names must be in the same order as the corresponding
annotation types in annotationTypes.
• outputDirectoryUrl - this enables the user to specify the export directory where the
file is exported with its original name and an extension (provided as a parameter)
appended at the end of filename. Note that you can also save a whole corpus in one
go. If not provided, use the temporary directory.
20.12
Annotation Set Transfer
The Annotation Set Transfer allows copying or moving annotations to a new annotation
set if they lie between the beginning and the end of an annotation of a particular type
(the covering annotation). For example, this can be used when a user only wants to run
a processing resource over a specific part of a document, such as the Body of an HTML
document. The user specifies the name of the annotation set and the annotation which
covers the part of the document they wish to transfer, and the name of the new annotation
set. All the other annotations corresponding to the matched text will be transferred to
the new annotation set. For example, we might wish to perform named entity recognition
on the body of an HTML text, but not on the headers. After tokenising and performing
gazetteer lookup on the whole text, we would use the Annotation Set Transfer to transfer
those annotations (created by the tokeniser and gazetteer) into a new annotation set, and
then run the remaining NE resources, such as the semantic tagger and coreference modules,
on them.
The Annotation Set Transfer has no loadtime parameters. It has the following runtime
parameters:
• inputASName - this defines the annotation set from which annotations will be transferred (copied or moved). If nothing is specified, the Default annotation set will be
used.
• outputASName - this defines the annotation set to which the annotations will be transferred. This default value for this parameter is ‘Filtered’. If it is left blank the Default
annotation set will be used.
• tagASName - this defines the annotation set which contains the annotation covering the
relevant part of the document to be transferred. This default value for this parameter
is ‘Original markups’. If it is left blank the Default annotation set will be used.
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• textTagName - this defines the type of the annotation covering the annotations to be
transferred. The default value for this parameter is ‘BODY’. If this is left blank, then
all annotations from the inputASName annotation set will be transferred. If more
than one covering annotation is found, the annotation covered by each of them will
be transferred. If no covering annotation is found, the processing depends on the
copyAllUnlessFound parameter (see below).
• copyAnnotations - this specifies whether the annotations should be moved or copied.
The default value false will move annotations, removing them from the inputASName
annotation set. If set to true the annotations will be copied.
• transferAllUnlessFound - this specifies what should happen if no covering annotation
is found. The default value is true. In this case, all annotations will be copied or moved
(depending on the setting of parameter copyAnnotations) if no covering annotation
is found. If set to false, no annotation will be copied or moved.
• annotationTypes - if annotation type names are specified for this list, only candidate
annotations of those types will be transferred or copied. If an entry in this list is specified in the form OldTypeName=NewTypeName, then annotations of type OldTypeName
will be selected for copying or transfer and renamed to NewTypeName in the output
annotation set.
For example, suppose we wish to perform named entity recognition on only the text covered
by the BODY annotation from the Original Markups annotation set in an HTML document.
We have to run the gazetteer and tokeniser on the entire document, because since these
resources do not depend on any other annotations, we cannot specify an input annotation
set for them to use. We therefore transfer these annotations to a new annotation set (Filtered)
and then perform the NE recognition over these annotations, by specifying this annotation
set as the input annotation set for all the following resources. In this example, we would
set the following parameters (assuming that the annotations from the tokenise and gazetteer
are initially placed in the Default annotation set).
• inputASName: Default
• outputASName: Filtered
• tagASName: Original markups
• textTagName: BODY
• copyAnnotations: true or false (depending on whether we want to keep the Token and
Lookup annotations in the Default annotation set)
• copyAllUnlessFound: true
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The AST PR makes a shallow copy of the feature map for each transferred annotation, i.e.
it creates a new feature map containing the same keys and values as the original. It does
not clone the feature values themselves, so if your annotations have a feature whose value
is a collection and you need to make a deep copy of the collection value then you will not
be able to use the AST PR to do this. Similarly if you are copying annotations and do in
fact want to share the same feature map between the source and target annotations then
the AST PR is not appropriate. In these sorts of cases a JAPE grammar or Groovy script
would be a better choice.
20.13
Schema Enforcer
One common use of the Annotation Set Transfer (AST) PR (see Section 20.12) is to create
a ‘clean’ or final annotation set for a GATE application, i.e. an annotation set containing
only those annotations which are required by the application without any temporary or
intermediate annotations which may also have been created. Whilst really useful the AST
suffers from two problems 1) it can be complex to configure and 2) it offers no support for
modifying or removing features of the annotations it copies.
Many GATE applications are developed through a process which starts with experts manually annotating documents in order for the application developer to understand what is
required and which can later be used for testing and evaluation. This is usually done using
either GATE Teamware or within GATE Developer using the Schema Annotation Editor
(Section 3.4.6). Either approach requires that each of the annotation types being created
is described by an XML based Annotation Schema. The Schema Enforcer (part of the
Schema Tools plugin) uses these same schemas to create an annotation set, the contents of
which, strictly matches the provided schemas.
The Schema Enforcer will copy an annotation if and only if....
• the type of the annotation matches one of the supplied schemas
• all required features are present and valid (i.e. meet the requirements for being copied
to the ’clean’ annotation)
Each feature of an annotation is copied to the new annotation if and only if....
• the feature name matches a feature in the schema describing the annotation
• the value of the feature is of the same type as specified in the schema
• if the feature is defined, in the schema, as an enumerated type then the value must
match one of the permitted values
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The Schema Enforcer has no initialization parameters and is configured via the following
runtime parameters:
• inputASName - - this defines the annotation set from which annotations will be copied.
If nothing is specified, the default annotation set will be used.
• outputASName - this defines the annotation set to which the annotations will be transferred. This must be an empty or non-existent annotation set.
• schmeas - a list of schemas that will be enforced when duplicating the input annotation
set.
• useDefaults - if true then the default value for required features (specified using the
value attribute in the XML schema) will be used to help complete an otherwise invalid
annotation, defaults to false.
Whilst this PR makes the creation of a clean output set easy (given the schemas) it is worth
noting that schemas can only define features which have basic types; string, integer, boolean,
float, double, short, and byte. This means that you cannot define a feature which has an
object as it’s value. For example, this prevents you defining a feature as a list of numbers.
If this is an issue then it is trivial to write JAPE to copy extra features not specified in the
schemas as the annotations have the same ID in both the input and output annotation sets.
An example JAPE file for copying the matches feature created by the Orthomatcher PR
(see Section 6.8) is provided.
20.14
Information Retrieval in GATE
GATE comes with a full-featured Information Retrieval (IR) subsystem that allows queries to
be performed against GATE corpora. This combination of IE and IR means that documents
can be retrieved from the corpora not only based on their textual content but also according
to their features or annotations. For example, a search over the Person annotations for
‘Bush’ will return documents with higher relevance, compared to a search in the content for
the string ‘bush’. The current implementation is based on the most popular open source
full-text search engine - Lucene (available at http://jakarta.apache.org/lucene/) but other
implementations may be added in the future.
An Information Retrieval system is most often considered a system that accepts as input a
set of documents (corpus) and a query (combination of search terms) and returns as input
only those documents from the corpus which are considered as relevant according to the
query. Usually, in addition to the documents, a proper relevance measure (score) is returned
for each document. There exist many relevance metrics, but usually documents which are
considered more relevant, according to the query, are scored higher.
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Figure 20.3: Documents with scores, returned from a search over a corpus
doc1
doc2
...
...
docn
term1
w1,1
w2,1
...
...
wn , 1
term2
w1,2
w2,1
...
...
wn,2
...
...
...
...
...
...
... termk
... w1,k
... w2,k
...
...
...
...
... wn,k
Table 20.2: An information retrieval document-term matrix
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Figure 20.3 shows the results from running a query against an indexed corpus in GATE.
Information Retrieval systems usually perform some preprocessing one the input corpus in
order to create the document-term matrix for the corpus. A document-term matrix is usually
presented as in Table 20.2, where doci is a document from the corpus, termj is a word that is
considered as important and representative for the document and wi, j is the weight assigned
to the term in the document. There are many ways to define the term weight functions,
but most often it depends on the term frequency in the document and in the whole corpus
(i.e. the local and the global frequency). Note that the machine learning plugin described in
Chapter 17 can produce such document-term matrix (for detailed description of the matrix
produced, see Section 17.2.4).
Note that not all of the words appearing in the document are considered terms. There
are many words (called ‘stop-words’) which are ignored, since they are observed too often
and are not representative enough. Such words are articles, conjunctions, etc. During the
preprocessing phase which identifies such words, usually a form of stemming is performed in
order to minimize the number of terms and to improve the retrieval recall. Various forms
of the same word (e.g. ‘play’, ‘playing’ and ‘played’) are considered identical and multiple
occurrences of the same term (probably ‘play’) will be observed.
It is recommended that the user reads the relevant Information Retrieval literature for a
detailed explanation of stop words, stemming and term weighting.
IR systems, in a way similar to IE systems, are evaluated with the help of the precision and
recall measures (see Section 10.1 for more details).
20.14.1
Using the IR Functionality in GATE
In order to run queries against a corpus, the latter should be ‘indexed’. The indexing process
first processes the documents in order to identify the terms and their weights (stemming is
performed too) and then creates the proper structures on the local file system. These file
structures contain indexes that will be used by Lucene (the underlying IR engine) for the
retrieval.
Once the corpus is indexed, queries may be run against it. Subsequently the index may be
removed and then the structures on the local file system are removed too. Once the index
is removed, queries cannot be run against the corpus.
Indexing the Corpus
In order to index a corpus, the latter should be stored in a serial datastore. In other words,
the IR functionality is unavailable for corpora that are transient or stored in a RDBMS
datastores (though support for the latter may be added in the future).
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To index the corpus, follow these steps:
• Select the corpus from the resource tree (top-left pane) and from the context menu
(right button click) choose ‘Index Corpus’. A dialogue appears that allows you to
specify the index properties.
• In the index properties dialogue, specify the underlying IR system to be used (only
Lucene is supported at present), the directory that will contain the index structures,
and the set of properties that will be indexed such as document features, content, etc
(the same properties will be indexed for each document in the corpus).
• Once the corpus in indexed, you may start running queries against it. Note that the
directory specified for the index data should exist and be empty. Otherwise an error
will occur during the index creation.
Figure 20.4: Indexing a corpus by specifying the index location and indexed features (and
content)
Querying the Corpus
To query the corpus, follow these steps:
• Create a SearchPR processing resource. All the parameters of SearchPR are runtime
so they are set later.
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• Create a “pipeline” application (not a “corpus pipeline”) containing the SearchPR.
• Set the following SearchPR parameters:
– The corpus that will be queried.
– The query that will be executed.
– The maximum number of documents returned.
A query looks like the following:
{+/-}field1:term1 {+/-}field2:term2 ? {+/-}fieldN:termN
where field is the name of a index field, such as the one specified at index creation
(the document content field is body) and term is a term that should appear in the
field.
For example the query:
+body:government +author:CNN
will inspect the document content for the term ‘government’ (together with variations
such as ‘governments’ etc.) and the index field named ‘author’ for the term ‘CNN’.
The ‘author’ field is specified at index creation time, and is either a document feature
or another document property.
• After the SearchPR is initialized, running the application executes the specified query
over the specified corpus.
• Finally, the results are displayed (see fig.1) after a double-click on the SearchPR processing resource.
Removing the Index
An index for a corpus may be removed at any time from the ‘Remove Index’ option of the
context menu for the indexed corpus (right button click).
20.14.2
Using the IR API
The IR API within GATE Embedded makes it possible for corpora to be indexed, queried and
results returned from any Java application, without using GATE Developer. The following
sample indexes a corpus, runs a query against it and then removes the index.
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1
2
3
4
5
6
// open a serial datastore
SerialDataStore sds =
Factory . openDataStore ( " gate . persist . SerialDataStore " ,
" / tmp / datastore1 " );
sds . open ();
7
8
9
10
//set an AUTHOR feature for the test document
Document doc0 = Factory . newDocument ( new URL ( " / tmp / documents / doc0 . html " ));
doc0 . getFeatures (). put ( " author " ," John Smith " );
11
12
13
Corpus corp0 = Factory . newCorpus ( " TestCorpus " );
corp0 . add ( doc0 );
14
15
16
17
//store the corpus in the serial datastore
Corpus serialCorpus = ( Corpus ) sds . adopt ( corp0 , null );
sds . sync ( serialCorpus );
18
19
//index the corpus - the content and the AUTHOR feature
20
21
IndexedCorpus indexedCorpus = ( IndexedCorpus ) serialCorpus ;
22
23
24
25
26
27
28
29
30
D e f a u l t I n d e x D e f i n i t i o n did = new D e f a u l t I n d e x D e f i n i t i o n ();
did . s e t I r E n g i n e C l a s s N a m e (
gate . creole . ir . lucene . LuceneIREngine . class . getName ());
did . setIndexLocation ( " / tmp / index1 " );
did . addIndexField ( new IndexField ( " content " ,
new D o c u m e n t C o n t e n t R e a d e r () , false ));
did . addIndexField ( new IndexField ( " author " , null , false ));
indexedCorpus . se tI nd ex De fi ni ti on ( did );
31
32
33
indexedCorpus . getIndexManager (). createIndex ();
//the corpus is now indexed
34
35
36
37
//search the corpus
Search search = new LuceneSearch ();
search . setCorpus ( ic );
38
39
QueryResultList res = search . search ( " + content : government + author : John " );
40
41
42
43
44
45
46
47
//get the results
Iterator it = res . getQueryResults ();
while ( it . hasNext ()) {
QueryResult qr = ( QueryResult ) it . next ();
System . out . println ( " DOCUMENT_ID = " + qr . getDocumentID ()
+ ",
score = " + qr . getScore ());
}
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20.15
Websphinx Web Crawler
The ‘Web Crawler Websphinx’ plugin enables GATE to build a corpus from a web crawl. It
is based on Websphinx, a JAVA-based, customizable, multi-threaded web crawler.
Note: if you are using this plugin via an IDE, you may need to make sure that the websphinx.jar file is on the IDE’s classpath, or add it to the IDE’s lib directory.
The basic idea is to specify a source URL (or set of documents created from web URLs) and
a depth and maximum number of documents to build the initial corpus upon which further
processing could be done. The PR itself provides a number of other parameters to regulate
the crawl.
This PR now uses the HTTP Content-Type headers to determine each web page’s encoding
and MIME type before creating a GATE Document from it. It also adds to each document a Date feature (with a java.util.Date value) based on the HTTP Last-Modified
header (if available) or the current timestamp, an originalMimeType feature taken from
the Content-Type header, and an originalLength feature indicating the size in bytes of the
downloaded document.
20.15.1
Using the Crawler PR
In order to use the processing resource you need to load the plugin using the plugin manager,
create an instance of the crawl PR from the list of processing resources, and create a corpus
in which to store crawled documents. In order to use the crawler, create a simple pipeline
(not a corpus pipeline) and add the crawl PR to the pipeline.
Once the crawl PR is created there will be a number of parameters that can be set based on
the PR required (see also Figure 20.5).
depth The depth (integer) to which the crawl should proceed.
dfs A boolean:
true the crawler visits links with a depth-first strategy;
false the crawler visits links with a breadth-first strategy;
domain An enum value, presented as a pull-down list in the GUI:
SUBTREE The crawler visits only the descendents of the pages specified as the roots
for the crawl.
WEB The crawler can visit any pages on the web.
SERVER The crawler can visit only pages that are present on the server where the
root pages are located.
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Figure 20.5: Crawler parameters
max The maximum number (integer) of pages to be kept: the crawler will stop when it has
stored this number of documents in the output corpus. Use −1 to ignore this limit.
maxPageSize The maximum page size in kB; pages over this limit will be ignored—even
as roots of the crawl—and their links will not be crawled. If your crawl does not add
any documents (even the seeds) to the output corpus, try increasing this value. (A 0
or negative value here means “no limit”.)
stopAfter The maximum number (integer) of pages to be fetched: the crawler will stop
when it has visited this number of pages. Use −1 to ignore this limit. If max >
stopAfter > 0 then the crawl will store at most stopAfter (not max ) documents.
root A string containing one URL to start the crawl.
source A corpus that contains the documents whose gate.sourceURL features will be used
to start the crawl. If you use both root and source parameters, both the root value
and the URLs collected from the source documents will seed the crawl.
outputCorpus The corpus in which the fetched documents will be stored.
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keywords A List<String> for matching against crawled documents. If this list is empty
or null, all documents fetched will be kept. Otherwise, only documents that contain
one of these strings will be stored in the output corpus. (Documents that are fetched
but not kept are still scanned for further links.)
keywordsCaseSensitive This boolean determines whether keyword matching is casesensitive or not.
convertXmlTypes GATE’s XmlDocumentFormat only accepts certain MIME types.
If this parameter is true, the crawl PR converts other XML types (such as
application/atom+xml.xml) to text/xml before trying to instantiate the GATE document (this allows GATE to handle RSS feeds, for example).
userAgent If this parameter is blank, the crawler will use the default Websphinx user-agent
header. Set this parameter to spoof the header.
Once the parameters are set, the crawl can be run and the documents fetched (and matched
to the keywords, if that list is in use) are added to the specified corpus. Documents that are
fetched but not matched are discarded after scanning them for further links.
Note that you must use a simple Pipeline, and not a Corpus Pipeline. In order to process
the corpus of crawled documents, you need to build a separate Corpus Pipeline and run it
after crawling. You could combine the two functions by carefully developing a Scriptable
Controller (see section 7.16.3 for details).
20.16
Google Plugin
This plugin is no longer operational because the functionality, provided by Google, on which
it depends, is no longer available.
20.17
Yahoo Plugin
The Yahoo API is now integrated with GATE, and can be used as a PR-based plugin. This
plugin, ‘Web Search Yahoo’, allows the user to query Yahoo and build a document corpus
that contains the search results returned by Yahoo for the query. For more information
about the Yahoo API please refer to http://developer.yahoo.com/search/. In order to use
the Yahoo PR, you need to obtain an application ID.
The Yahoo PR can be used for a number of different application scenarios. For example,
one use case is where a user wants to find the different named entities that can be associated
with a particular individual. In this example, the user could build a collection of documents
by querying Yahoo with the individual’s name and then running ANNIE over the collection.
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This would annotate the results and show the different Organization, Location and other
entities that are associated with the query.
20.17.1
Using the YahooPR
In order to use the PR, you first need to load the plugin using the GATE Developer plugin
manager. Once the PR is loaded, it can be initialized by creating an instance of a new PR.
Here you need to specify the Yahoo Application ID. Please use the license key assigned to
you by registering with Yahoo.
Once the Yahoo PR is initialized, it can be placed in a pipeline or a conditional pipeline
application. This pipeline would contain the instance of the Yahoo PR just initialized as
above. There are a number of parameters to be set at runtime:
• corpus: The corpus used by the plugin to add or append documents from the Web.
• corpusAppendMode: If set to true, will append documents to the corpus. If set to
false, will remove preexisting documents from the corpus, before adding the documents newly fetched by the PR
• limit: A limit on the results returned by the search. Default set to 10.
• pagesToExclude: This is an optional parameter. It is a list with URLs not to be
included in the search.
• query: The query sent to Yahoo. It is in the format accepted by Yahoo.
Once the required parameters are set we can run the pipeline. This will then download all
the URLs in the results and create a document for each. These documents would be added
to the corpus.
20.18
Google Translator PR
The Google Translator PR allows users to translate their documents into many other
languages using the Google translation service.
It is based on the library called
google-translate-api-java which is distributed under the LGPL licence and is available
to download from http://code.google.com/p/google-api-translate-java/.
The PR is included in the plugin called Web Translate Google and depends on the Alignment
plugin. (chapter 18).
If a user wants to translate an English document into French using the Google Translator
PR. The first thing user needs to do is to create an instance of CompoundDocument with
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the English document as a member of it. The CompoundDocument in GATE provides a
convenient way to group parallel documents that are translations of one other (see chapter
18 for more information). The idea is to use text from one of the members of the provided
compound document, translate it using the google translation service and create another
member with the translated text. In the process, the PR also aligns the chunks of parallel
texts. Here, a chunk could be a sentence, paragraph, section or the entire document.
siteReferrer is the only init-time parameter required to instantiate the PR. It has to be
a valid website address. The value of this parameter is required to inform Google about the
users using their service. There are seven run-time parameters:
• document - an instance of the compound document with a member document containing source text.
• sourceDocumentId - id of the source member document that needs to be translated.
• targetDocumentId - id of the target member document. This document is created by
the PR and contains the translated text.
• sourceLanguage - the language of the source document.
• targetLanguage - the language into which the source document should be translated.
• unitOfTranslation - annotation type used for identifying chunks of texts to be translated and aligned.
• inputASName - name of the annotation set which contains unit of translations.
• alignmentFeatureName - name of the alignment feature used for storing the alignment
information. The alignment feature is a document feature stored on the compound
document.
20.19
WordNet in GATE
GATE currently supports versions 1.6 and newer of WordNet, so in order to use WordNet in
GATE, you must first install a compatible version of WordNet on your computer. WordNet
is available at http://wordnet.princeton.edu/. The next step is to configure GATE to work
with your local WordNet installation. Since GATE relies on the Java WordNet Library
(JWNL) for WordNet access, this step consists of providing one special xml file that is used
internally by JWNL. This file describes the location of your local copy of the WordNet index
files. An example of this wn-config.xml file is shown below:
<?xml version="1.0" encoding="UTF-8"?>
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Figure 20.6: WordNet in GATE – results for ‘bank’
<jwnl_properties language="en">
<version publisher="Princeton" number="3.0" language="en"/>
<dictionary class="net.didion.jwnl.dictionary.FileBackedDictionary">
<param name="morphological_processor"
value="net.didion.jwnl.dictionary.morph.DefaultMorphologicalProcessor">
<param name="operations">
<param value=
"net.didion.jwnl.dictionary.morph.LookupExceptionsOperation"/>
<param value="net.didion.jwnl.dictionary.morph.DetachSuffixesOperation">
<param name="noun"
value="|s=|ses=s|xes=x|zes=z|ches=ch|shes=sh|men=man|ies=y|"/>
<param name="verb"
value="|s=|ies=y|es=e|es=|ed=e|ed=|ing=e|ing=|"/>
<param name="adjective"
value="|er=|est=|er=e|est=e|"/>
<param name="operations">
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Figure 20.7: WordNet in GATE
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<param
value="net.didion.jwnl.dictionary.morph.LookupIndexWordOperation"/>
<param
value="net.didion.jwnl.dictionary.morph.LookupExceptionsOperation"/>
</param>
</param>
<param value="net.didion.jwnl.dictionary.morph.TokenizerOperation">
<param name="delimiters">
<param value=" "/>
<param value="-"/>
</param>
<param name="token_operations">
<param
value="net.didion.jwnl.dictionary.morph.LookupIndexWordOperation"/>
<param
value="net.didion.jwnl.dictionary.morph.LookupExceptionsOperation"/>
<param
value="net.didion.jwnl.dictionary.morph.DetachSuffixesOperation">
<param name="noun"
value="|s=|ses=s|xes=x|zes=z|ches=ch|shes=sh|men=man|ies=y|"/>
<param name="verb"
value="|s=|ies=y|es=e|es=|ed=e|ed=|ing=e|ing=|"/>
<param name="adjective" value="|er=|est=|er=e|est=e|"/>
<param name="operations">
<param value=
"net.didion.jwnl.dictionary.morph.LookupIndexWordOperation"/>
<param value=
"net.didion.jwnl.dictionary.morph.LookupExceptionsOperation"/>
</param>
</param>
</param>
</param>
</param>
</param>
<param name="dictionary_element_factory" value=
"net.didion.jwnl.princeton.data.PrincetonWN17FileDictionaryElementFactory"/>
<param name="file_manager" value=
"net.didion.jwnl.dictionary.file_manager.FileManagerImpl">
<param name="file_type" value=
"net.didion.jwnl.princeton.file.PrincetonRandomAccessDictionaryFile"/>
<param name="dictionary_path" value="/home/mark/WordNet-3.0/dict/"/>
</param>
</dictionary>
<resource class="PrincetonResource"/>
</jwnl_properties>
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There are three things in this file which you need to configure based upon the version of
WordNet you wish to use. Firstly change the number attribute of the version element to
match the version of WordNet you are using. Then edit the value of the dictionary_path
parameter to point to your local installation of WordNet. Finally, if you want to use version 1.6 of WordNet then you allso need to alter the dictionary_element_factory to
use net.didion.jwnl.princeton.data.PrincetonWN16FileDictionaryElementFactory.
For full details of the format of the configuration file see the JWNL documentation
at http://sourceforge.net/projects/jwordnet.
After configuring GATE to use WordNet, you can start using the built-in WordNet browser
or API. In GATE Developer, load the WordNet plugin via the Plugin Management Console.
Then load WordNet by selecting it from the set of available language resources. Set the
value of the parameter to the path of the xml properties file which describes the WordNet
location (wn-config).
Once WordNet is loaded in GATE Developer, the well-known interface of WordNet will appear. You can search Word Net by typing a word in the box next to to the label ‘SearchWord”
and then pressing ‘Search’. All the senses of the word will be displayed in the window below.
Buttons for the possible parts of speech for this word will also be activated at this point.
For instance, for the word ‘play’, the buttons ‘Noun’, ‘Verb’ and ‘Adjective’ are activated.
Pressing one of these buttons will activate a menu with hyponyms, hypernyms, meronyms
for nouns or verb groups, and cause for verbs, etc. Selecting an item from the menu will
display the results in the window below.
To upgrade any existing GATE applications to use this improved WordNet plugin simply
replace your existing configuration file with the example above and configure for WordNet
1.6. This will then give results identical to the previous version – unfortunately it was not
possible to provide a transparent upgrade procedure.
More information about WordNet can be found at http://wordnet.princeton.edu/
More information about the JWNL library can be found at http://sourceforge.net/
projects/jwordnet
An example of using the WordNet API in GATE is available on the GATE examples page
at http://gate.ac.uk/wiki/code-repository/index.html.
20.19.1
The WordNet API
GATE Embedded offers a set of classes that can be used to access the WordNet Lexical
Database. The implementation of the GATE API for WordNet is based on Java WordNet
Library (JWNL). There are just a few basic classes, as shown in Figure 20.8. Details about
the properties and methods of the interfaces/classes comprising the API can be obtained
from the JavaDoc. Below is a brief overview of the interfaces:
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• WordNet: the main WordNet class. Provides methods for getting the synsets of a
lemma, for accessing the unique beginners, etc.
• Word: offers access to the word’s lemma and senses
• WordSense: gives access to the synset, the word, POS and lexical relations.
• Synset: gives access to the word senses (synonyms) in the synset, the semantic relations, POS etc.
• Verb: gives access to the verb frames (not working properly at present)
• Adjective: gives access to the adj. position (attributive, predicative, etc.).
• Relation: abstract relation such as type, symbol, inverse relation, set of POS tags,
etc. to which it is applicable.
• LexicalRelation
• SemanticRelation
• VerbFrame
Figure 20.8: The Wordnet API
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20.20
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Kea - Automatic Keyphrase Detection
Kea is a tool for automatic detection of key phrases developed at the University of Waikato in
New Zealand. The home page of the project can be found at http://www.nzdl.org/Kea/.
This user guide section only deals with the aspects relating to the integration of Kea in
GATE. For the inner workings of Kea, please visit the Kea web site and/or contact its
authors.
In order to use Kea in GATE Developer, the ‘Keyphrase Extraction Algorithm’ plugin needs
to be loaded using the plugins management console. After doing that, two new resource types
are available for creation: the ‘KEA Keyphrase Extractor’ (a processing resource) and the
‘KEA Corpus Importer’ (a visual resource associated with the PR).
20.20.1
Using the ‘KEA Keyphrase Extractor’ PR
Kea is based on machine learning and it needs to be trained before it can be used to extract
keyphrases. In order to do this, a corpus is required where the documents are annotated
with keyphrases. Corpora in the Kea format (where the text and keyphrases are in separate
files with the same name but different extensions) can be imported into GATE using the
‘KEA Corpus Importer’ tool. The usage of this tool is presented in a subsection below.
Once an annotated corpus is obtained, the ‘KEA Keyphrase Extractor’ PR can be used to
build a model:
1. load a ‘KEA Keyphrase Extractor’
2. create a new ‘Corpus Pipeline’ controller.
3. set the corpus for the controller
4. set the ‘trainingMode’ parameter for the PR to ‘true’
5. run the application.
After these steps, the Kea PR contains a trained model. This can be used immediately by
switching the ‘trainingMode’ parameter to ‘false’ and running the PR over the documents
that need to be annotated with keyphrases. Another possibility is to save the model for later
use, by right-clicking on the PR name in the right hand side tree and choosing the ‘Save
model’ option.
When a previously built model is available, the training procedure does not need to be
repeated, the existing model can be loaded in memory by selecting the ‘Load model’ option
in the PR’s context menu.
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Figure 20.9: Parameters used by the Kea PR
The Kea PR uses several parameters as seen in Figure 20.9:
document The document to be processed.
inputAS The input annotation set. This parameter is only relevant when the PR is running in training mode and it specifies the annotation set containing the keyphrase
annotations.
outputAS The output annotation set. This parameter is only relevant when the PR is
running in application mode (i.e. when the ‘trainingMode’ parameter is set to false)
and it specifies the annotation set where the generated keyphrase annotations will be
saved.
minPhraseLength the minimum length (in number of words) for a keyphrase.
minNumOccur the minimum number of occurrences of a phrase for it to be a keyphrase.
maxPhraseLength the maximum length of a keyphrase.
phrasesToExtract how many different keyphrases should be generated.
keyphraseAnnotationType the type of annotations used for keyphrases.
dissallowInternalPeriods should internal periods be disallowed.
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Figure 20.10: Options for the ‘KEA Corpus Importer’
trainingMode if ‘true’ the PR is running in training mode; otherwise it is running in
application mode.
useKFrequency should the K-frequency be used.
20.20.2
Using Kea Corpora
The authors of Kea provide on the project web page a few manually annotated corpora that
can be used for training Kea. In order to do this from within GATE, these corpora need to
be converted to the format used in GATE (i.e. GATE documents with annotations). This
is possible using the ‘KEA Corpus Importer’ tool which is available as a visual resource
associated with the Kea PR. The importer tool can be made visible by double-clicking on
the Kea PR’s name in the resources tree and then selecting the ‘KEA Corpus Importer’ tab,
see Figure 20.10.
The tool will read files from a given directory, converting the text ones into GATE documents
and the ones containing keyphrases into annotations over the documents.
The user needs to specify a few values:
Source Directory the directory containing the text and key files. This can be typed in or
selected by pressing the folder button next to the text field.
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Extension for text files the extension used for text fields (by default .txt).
Extension for keyphrase files the extension for the files listing keyphrases.
Encoding for input files the encoding to be used when reading the files.
Corpus name the name for the GATE corpus that will be created.
Output annotation set the name for the annotation set that will contain the keyphrases
read from the input files.
Keyphrase annotation type the type for the generated annotations.
20.21
Ontotext JapeC Compiler
Note: the JapeC compiler does not currently support the new JAPE language features introduced in July–September 2008. If you need to use negation, the @length and @string
accessors, the contextual operators within and contains, or any comparison operators other
than ==, then you will need to use the standard JAPE transducer instead of JapeC.
JapeC is an alternative implementation of the JAPE language which works by compiling
JAPE grammars into Java code. Compared to the standard implementation, these compiled
grammars can be several times faster to run. At Ontotext, a modified version of the ANNIE
sentence splitter using compiled grammars has been found to run up to five times as fast
as the standard version. The compiler can be invoked manually from the command line, or
used through the ‘Ontotext Japec Compiler’ PR in the Jape Compiler plugin.
The ‘Ontotext Japec Transducer’ (com.ontotext.gate.japec.JapecTransducer) is a processing
resource that is designed to be an alternative to the original Jape Transducer. You can
simply replace gate.creole.Transducer with com.ontotext.gate.japec.JapecTransducer in your
gate application and it should work as expected.
The Japec transducer takes the same parameters as the standard JAPE transducer:
grammarURL the URL from which the grammar is to be loaded. Note that the Japec
Transducer will only work on file: URLs. Also, the alternative binaryGrammarURL
parameter of the standard transducer is not supported.
encoding the character encoding used to load the grammars.
ontology the ontology used for ontolog-aware transduction.
Its runtime parameters are likewise the same as those of the standard transducer:
document the document to process.
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inputASName name of the AnnotationSet from which input annotations to the transducer
are read.
outputASName name of the AnnotationSet to which output annotations from the transducer are written.
The Japec compiler itself is written in Haskell. Compiled binaries are provided for Windows,
Linux (x86) and Mac OS X (PowerPC), so no Haskell interpreter is required to run Japec
on these platforms. For other platforms, or if you make changes to the compiler source code,
you can build the compiler yourself using the Ant build file in the Jape Compiler plugin
directory. You will need to install the latest version of the Glasgow Haskell Compiler2 and
associated libraries. The japec compiler can then be built by running:
../../bin/ant japec.clean japec
from the Jape Compiler plugin directory.
20.22
Annotation Merging Plugin
If we have annotations about the same subject on the same document from different annotators, we may need to merge the annotations.
This plugin implements two approaches for annotation merging.
MajorityVoting takes a parameter numMinK and selects the annotation on which at least
numMinK annotators agree. If two or more merged annotations have the same span, then
the annotation with the most supporters is kept and other annotations with the same span
are discarded.
MergingByAnnotatorNum selects one annotation from those annotations with the same span,
which the majority of the annotators support. Note that if one annotator did not create the
annotation with the particular span, we count it as one non-support of the annotation with
the span. If it turns out that the majority of the annotators did not support the annotation
with that span, then no annotation with the span would be put into the merged annotations.
The annotation merging methods are available via the Annotation Merging plugin. The
plugin can be used as a PR in a pipeline or corpus pipeline. To use the PR, each document
in the pipeline or the corpus pipeline should have the annotation sets for merging. The
annotation merging PR has no loading parameters but has several run-time parameters,
explained further below.
The annotation merging methods are implemented in the GATE API, and are available in
GATE Embedded as described in Section 7.18.
2
GHC version 6.4.1 was used to build the supplied binaries for Windows, Linux and Mac
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Parameters
• annSetOutput: the annotation set in the current document for storing the merged
annotations. You should not use an existing annotation set, as the contents may be
deleted or overwritten.
• annSetsForMerging: the annotation sets in the document for merging. It is an optional
parameter. If it is not assigned with any value, the annotation sets for merging would be
all the annotation sets in the document except the default annotation set. If specified,
it is a sequence of the names of the annotation sets for merging, separated by ‘;’. For
example, the value ‘a-1;a-2;a-3’ represents three annotation set, ‘a-1’, ‘a-2’ and ‘a-3’.
• annTypeAndFeats: the annotation types in the annotation set for merging. It is
an optional parameter. It specifies the annotation types in the annotation sets
for merging. For each type specified, it may also specify an annotation feature
of the type. The parameter is a sequence of names of annotation types, separated by ‘;’. A single annotation feature can be specified immediately following
the annotation type’s name, separated by ‘->’ in the sequence. For example, the
value ‘SENT->senRel;OPINION OPR;OPINION SRC->type’ specifies three annotation types, ‘SENT’, ‘OPINION OPR’ and ‘OPINION SRC’ and specifies the annotation feature ‘senRel’ and ‘type’ for the two types SENT and OPINION SRC, respectively but does not specify any feature for the type OPINION OPR. If the annTypeAndFeats parameter is not set, the annotation types for merging are all the types in
the annotation sets for merging, and no annotation feature for each type is specified.
• keepSourceForMergedAnnotations: should source annotations be kept in the annSetsForMerging annotation sets when merged? True by default.
• mergingMethod: specifies the method used for merging. Possible values are MajorityVoting and MergingByAnnotatorNum, referring to the two merging methods described above, respectively.
• minimalAnnNum: specifies the minimal number of annotators who agree on one annotation in order to put the annotation into merged set, which is needed by the merging
method MergingByAnnotatorNum. If the value of the parameter is smaller than 1, the
parameter is taken as 1. If the value is bigger than total number of annotation sets
for merging, it is taken to be total number of annotation sets. If no value is assigned,
a default value of 1 is used. Note that the parameter does not have any effect on the
other merging method MajorityVoting.
20.23
Copying Annotations between Documents
Sometimes a document has two copies, each of which was annotated by different annotators
for the same task. We may want to copy the annotations in one copy to the other copy of the
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document. This could be in order to use less resources, or so that we can process them with
some other plugin, such as annotation merging or IAA. The Copy Annots Between Docs
plugin does exactly this.
The plugin is available with the GATE distribution. When loading the plugin into GATE,
it is represented as a processing resource, Copy Anns to Another Doc PR. You need
to put the PR into a Corpus Pipeline to use it. The plugin does not have any initialisation
parameters. It has several run-time parameters, which specify the annotations to be copied,
the source documents and target documents. In detail, the run-time parameters are:
• sourceFilesURL specifies a directory in which the source documents are in. The
source documents must be GATE xml documents. The plugin copies the annotations
from these source documents to target documents.
• inputASName specifies the name of the annotation set in the source documents.
Whole annotations or parts of annotations in the annotation set will be copied.
• annotationTypes specifies one or more annotation types in the annotation set inputASName which will be copied into target documents. If no value is given, the plugin
will copy all annotations in the annotation set.
• outputASName specifies the name of the annotation set in the target documents,
into which the annotations will be copied. If there is no such annotation set in the
target documents, the annotation set will be created automatically.
The Corpus parameter of the Corpus Pipeline application containing the plugin specifies a
corpus which contains the target documents. Given one (target) document in the corpus,
the plugin tries to find a source document in the source directory specified by the parameter
sourceFilesURL, according to the similarity of the names of the source and target documents.
The similarity of two file names is calculated by comparing the two strings of names from the
start to the end of the strings. Two names have greater similarity if they share more characters from the beginning of the strings. For example, suppose two target documents have the
names aabcc.xml and abcab.xml and three source files have names abacc.xml, abcbb.xml and
aacc.xml, respectively. Then the target document aabcc.xml has the corresponding source
document aacc.xml, and abcab.xml has the corresponding source document abcbb.xml.
20.24
OpenCalais Plugin
OpenCalais provides a web service for semantic annotation of text. The user submits a
document to the web service, which returns entity and relations annotations in RDF, JSON
or some other format. Typically, users integrate OpenCalais annotation of their web pages
to provide additional links and ‘semantic functionality’. OpenCalais can be found at http:
//www.opencalais.com
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The GATE OpenCalais PR submits a GATE document to the OpenCalais web service,
and adds the annotations from the OpenCalais response as GATE annotations in the GATE
document. It therefore provides OpenCalais semantic annotation functionality within GATE,
for use by other PRs.
The PR only supports OpenCalais entities, not relations - although this should be straightforward for a competent Java programmer to add. Each OpenCalais entity is represented in
GATE as an OpenCalais annotation, with features as given in the OpenCalais documentation.
The PR can be loaded with the CREOLE plugin manager dialog, from the creole directory
in the gate distribution, gate/plugins/Tagger OpenCalais. In order to use the PR, you will
need to have an OpenCalais account, and request an OpenCalais service key. You can do this
from the OpenCalais web site at http://www.opencalais.com. Provide your service key
as an initialisation parameter when you create a new OpenCalais PR in GATE. OpenCalais
make restrictions on the the number of requests you can make to their web service. See the
OpenCalais web page for details.
Initialisation parameters are:
• openCalaisURL This is the URL of the OpenCalais REST service, and should not
need to be changed - unless OpenCalais moves it!
• licenseID Your OpenCalais service key. This has to be requested from OpenCalais
and is specific to you.
Various runtime parameters are available from the OpenCalais API, and are named the same
as in that API. See the OpenCalais documentation for further details.
20.25
LingPipe Plugin
LingPipe is a suite of Java libraries for the linguistic analysis of human language3 . We
have provided a plugin called ‘LingPipe’ with wrappers for some of the resources available
in the LingPipe library. In order to use these resources, please load the ‘LingPipe’ plugin.
Currently, we have integrated the following five processing resources.
• LingPipe Tokenizer PR
• LingPipe Sentence Splitter PR
• LingPipe POS Tagger PR
3
see http://alias-i.com/lingpipe/
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• LingPipe NER PR
• LingPipe Language Identifier PR
Please note that most of the resources in the LingPipe library allow learning of new models.
However, in this version of the GATE plugin for LingPipe, we have only integrated the
application functionality. You will need to learn new models with Lingpipe outside of GATE.
We have provided some example models under the ‘resources’ folder which were downloaded
from LingPipe’s website. For more information on licensing issues related to the use of these
models, please refer to the licensing terms under the LingPipe plugin directory.
The LingPipe system can be loaded from the GATE GUI by simply selecting the ‘Load
LingPipe System’ menu item under the ‘File’ menu. This is similar to loading the ANNIE
application with default values.
20.25.1
LingPipe Tokenizer PR
As the name suggests this PR tokenizes document text and identifies the boundaries of
tokens. Each token is annotated with an annotation of type ‘Token’. Every annotation has a
feature called ‘length’ that gives a length of the word in number of characters. There are no
initialization parameters for this PR. The user needs to provide the name of the annotation
set where the PR should output Token annotations.
20.25.2
LingPipe Sentence Splitter PR
As the name suggests, this PR splits document text in sentences. It identifies sentence
boundaries and annotates each sentence with an annotation of type ‘Sentence’. There are
no initialization parameters for this PR. The user needs to provide name of the annotation
set where the PR should output Sentence annotations.
20.25.3
LingPipe POS Tagger PR
The LingPipe POS Tagger PR is useful for tagging individual tokens with their respective
part of speech tags. Each document must already have been processed with a tokenizer and
a sentence splitter (any kinds in GATE, not necessarily the LingPipe ones) since this PR
has Token and Sentence annotations as prerequisites. This PR adds a category feature to
each token.
This PR requires a model (dataset from training the tagger on a tagged corpus), which
must be provided as an initialization parameter. Several models are included in this plugin’s
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resources directory. Additional models can be downloaded from the LingPipe website4 or
trained according to LingPipe’s instructions5 .
Two models for Bulgarian are now available in GATE: bulgarian-full.model and
bulgarian-simplified.model, trained on a transformed version of the BulTreeBankDP [Osenova & Simov 04, Simov & Osenova 03, Simov et al. 02, Simov et al. 04a]. The full
model uses the complete tagset [Simov et al. 04b] whereas the simplified model uses tags
truncated before any hyphens (for example, Pca--p, Pca--s-f, Pca--s-m, Pca--s-n, and
Pce-as-m are all merged to Pca) to improve performance. This reduces the set from 573 to
249 tags and saves memory.
This PR has the following run-time parameters.
inputASName The name of the annotation set with Token and Sentence annotations.
applicationMode The POS tagger can be applied on the text in three different modes.
FIRSTBEST The tagger produces one tag for each token (the one that it calculates
is best) and stores it as a simple String in the category feature.
CONFIDENCE The tagger produces the best five tags for each token, with confidence scores, and stores them as a Map<String, Double> in the category feature.
This application mode requires more memory than the others.
NBEST The tagger produces the five best taggings for the whole document and
then stores one to five tags for each token (with document-based scores) as a
Map<String, List<Double>> in the category feature. This application mode is
noticeably slower than the others.
20.25.4
LingPipe NER PR
The LingPipe NER PR is used for named entity recognition. The PR recognizes entities
such as Persons, Organizations and Locations in the text. This PR requires a model which
it then uses to classify text as different entity types. An example model is provided under
the ‘resources’ folder of this plugin. It must be provided at initialization time. Similar to
other PRs, this PR expects users to provide name of the annotation set where the PR should
output annotations.
20.25.5
LingPipe Language Identifier PR
As the name suggests, this PR is useful for identifying the language of a document or span
of text. This PR uses a model file to identify the language of a text. A model is provided
4
5
http://alias-i.com/lingpipe/web/models.html
http://alias-i.com/lingpipe/demos/tutorial/posTags/read-me.html
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in this plugin’s resources/models subdirectory and as the default value of this required
initialization parameter. The PR has the following runtime parameters.
annotationType If this is supplied, the PR classifies the text underlying each annotation
of the specified type and stores the result as a feature on that annotation. If this is
left blank (null or empty), the PR classifies the text of each document and stores the
result as a document feature.
annotationSetName The annotation set used for input and output; ignored if annotationType is blank.
languageIdFeatureName The name of the document or annotation feature used to store
the results.
Unlike most other PRs (which produce annotations), this one adds either document features
or annotation features. (To classify both whole documents and spans within them, use
two instances of this PR.) Note that classification accuracy is better over long spans of
text (paragraphs rather than sentences, for example). More information on the languages
supported can be found in the LingPipe documentation.
20.26
OpenNLP Plugin
OpenNLP provides java-based tools for sentence detection, tokenization, pos-tagging, chunking and parsing, named-entity detection, and coreference. See the OpenNLP website for
details: http://opennlp.sourceforge.net/. The tools use the Maxent machine learning
package. See http://maxent.sourceforge.net/ for details.
In order to use these tools as GATE processing resources, load the ‘OpenNLP’ plugin via
the Plugin Management Console. Alternatively, the OpenNLP system can be loaded from
the GATE GUI by simply selecting the ‘Load OpenNLP System’ menu item under the ‘File’
menu. The OpenNLP PRs will be loaded, together with a pre-configured corpus pipeline
application containing these PRs. This is similar to loading the ANNIE application with
default values.
We have integrated the following five processing resources:
• OpenNlpTokenizer Tokenizer PR
• OpenNlpSentenceSplit Sentence Splitter PR
• OpenNlpPOS POS Tagger PR
• OpenNlpNameFinder NER PR
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• OpenNlpChunker Chunker PR
Note that in general, these PRs can be mixed with other PRs of similar types. For example,
you could create a pipeline that uses the OpenNLP Tokenizer, and the ANNIE POS Tagger.
You may occasionally have problems with some combinations. Notes on compatibility and
PR prerequisites are given for each PR in Section 20.26.2.
Note also that some of the OpenNLP tools use quite large machine learning models, which
the PRs need to load into memory. You may find that you have to give additional memory
to GATE in order to use the OpenNLP PRs comfortably. See Section 2.7.5 for an example
of how to do this.
Below, we describe the parameters common to all of the OpenNLP PRs. This is followed by
a section which gives brief details of each PR. For more details on each, see the OpenNLP
website, http://opennlp.sourceforge.net/.
20.26.1
Parameters common to all PRs
Load-time Parameters
All OpenNLP PRs have a ‘model’ parameter, which takes a URL. The URL should reference
a valid Maxent model, or in the case of the Name Finder a directory containing a set of models
for the different types of name sought. Default models can be found in the ‘models/english’
directory. In addition, the OpenNlpPOS POS Tagger PR has a ‘dictionary’ parameter, which
also takes a URL, and a ‘dictionaryEncoding’ parameter giving the character encoding of
the dictionary file. The default can be found in the ‘models/english’ directory.
For details of training new models (outside of the GATE framework), see Section 20.26.3
Run-time Parameters
The OpenNLP PRs have runtime parameters to specify the annotation sets they should use
for input and/or output. These are detailed below in the description of each PR, but all
PRs will use the default unnamed annotation set unless told otherwise.
20.26.2
OpenNLP PRs
OpenNlpTokenizer - Tokenizer PR
This PR adds Token annotations to the annotation set specified by the annotationSetName
parameter.
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This PR does not require any other PR to be run beforehand. It creates annotations of
type Token, with a feature and value ‘source=openNLP’ and a string feature that takes the
underlying string as its value.
OpenNlpSentenceSplit - Sentence Splitter PR
This PR adds Sentence annotations to the annotation set specified by the annotationSetName
parameter.
This PR does not require any other PR to be run beforehand. It creates annotations of type
Sentence, with a feature and value ‘source=openNLP’. If the OpenNLP sentence splitter
returns no output (i.e. fails to find any sentences), then GATE will add a single sentence
annotation from offset 0 to the end of the document. This is to prevent downstream PRs
that require sentence annotations from failing.
OpenNlpPOS - POS Tagger PR
This PR adds a feature for Part Of Speech to Token annotations.
This PR requires Sentence and Token annotations to be present in the annotation set specified by its annotationSetName parameter before it will work. These Sentence and Token
annotations do not have to be from another OpenNLP PR. They could, for example, be
from the ANNIE PRs. This PR adds a ‘category’ feature to each Token, with the predicted
Part Of Speech as value.
OpenNlpNameFinder - NER PR
This PR finds standard Named Entities, adding them as annotations named for each named
Entity type.
This PR requires Sentence and Token annotations to be present in the annotation set specified by its inputASName parameter before it will work. These Sentence and Token annotations do not have to be from another OpenNLP PR. They could, for example, be from
the ANNIE PRs. You may find, however, that not all pairings of Tokenizer and Sentence
Splitter will work successfully.
The Token annotations do not need to have a ‘category’ feature. In other words, you do not
need to run a POS Tagger before using this PR.
This PR creates annotations for each named entity in the annotation set specified by the
outputASName parameter, with a feature and value ‘source=openNLP’. It also adds a feature
of ‘type’ to each annotation, with the same values as the annotation type. Types include:
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• person
• organization
• location
• date
• time
• percentage
For full details of all types, see the OpenNLP website, http://opennlp.sourceforge.net/.
OpenNlpChunker - Chunker PR
This PR finds noun, verb, and other chunks, adding their position as features Token annotations.
This PR requires Sentence and Token annotations to be present in the annotation set specified by the annotationSetName parameter before it will work. The Token annotations need
to have a ‘category’ feature. In other words, you also need to run a POS Tagger before using
this PR. The Sentence and Token annotations (and ‘category’ POS tag features) do not have
to be from another OpenNLP PR. They could, for example, be from the ANNIE PRs.
This PR creates features of type ‘chunk’ for each token. The value of this feature define
whether the token is at the beginning of a chunk, inside a chunk, or outside a chunk, using
the standard BIO model. Example values and their interpretations are:
• B-NP Token is the first (beginning) token of a Noun Phrase
• I-NP Token is inside a Noun Phrase
• B-VP Token is the first (beginning) token of a Verb Phrase
• I-VP Token is inside a Verb Phrase
• O Token is outside any phrase
• B-PP Token is the first (beginning) token of a Prepositional Phrase
• B-ADVP Token is the first (beginning) token of an Adverbial Phrase
For full details of all chunk values, see the OpenNLP website, http://opennlp.
sourceforge.net/.
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Training new models
Within the OpenNLP framework, new models can be trained for each of the tools. By default,
the GATE PRs use the standard Maxent models for English which can be found in the
plugin’s ‘models/english’ directory. The models are copies of those in the ”models” module
in the OpenNLP CVS repository at http://opennlp.cvs.sourceforge.net. If you need to
train a different model for an OpenNLP PR, you will have to do this outside of GATE, and
then use the file URL of your new model as a value for the ‘model’ parameter of the PR. For
details on how to train models, see the Maxent website http://maxent.sourceforge.net/.
20.27
Tagger MetaMap Plugin
MetaMap, from the National Library of Medicine (NLM), maps biomedical text to the
UMLS Metathesaurus and allows Metathesaurus concepts to be discovered in a text
corpus.
The Tagger MetaMap plugin for GATE wraps the MetaMap Java API client to allow GATE
to communicate with a remote (or local) MetaMap PrologBeans mmserver10 and MetaMap
distribution. This allows the content of specified annotations (or the entire document content) to be processed by MetaMap and the results converted to GATE annotations and
features.
To use this plugin, you will need access to a remote MetaMap server, or install one locally
by downloading and installing the complete distribution:
http://metamap.nlm.nih.gov/
and Java PrologBeans mmserver
http://metamap.nlm.nih.gov/README_javaapi.html
The default mmserver10 location and port locations are localhost and 8066. To use a
different server location and/or port, see the above API documentation and specify the
--metamap server host and --metamap server port options within the metaMapOptions run-time parameter.
20.27.1
Run-time parameters
1. annotateNegEx: set this to true to add NegEx features to annotations (NegExType
and NegExTrigger). See http://code.google.com/p/negex/ for more information
on NegEx
2. annotatePhrases: set to true to output MetaMap phrase-level annotations (generally
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noun-phrase chunks). Only phrases containing a MetaMap mapping will be annotated.
Can be useful for post-coordination of phrase-level terms that do not exist in a precoordinated form in UMLS.
3. inputASName: input Annotation Set name.
Use in conjunction with inputASTypes: (see below). Unless specified, the entire document content will be
sent to MetaMap.
4. inputASTypes: only send the content of these annotations within inputASName
to MetaMap and add new MetaMap annotations inside each. Unless specified, the
entire document content will be sent to MetaMap.
5. inputASTypeFeature: send the content of this feature within inputASTypes
to MetaMap and wrap a new MetaMap annotation around each annotation in inputASTypes. If the feature is empty or does not exist, then the annotation content is
sent instead.
6. metaMapOptions: set parameter-less MetaMap options here. Default is -Xdt (truncate Candidates mappings, disallow derivational variants and do not use full text
parsing). See http://metamap.nlm.nih.gov/README_javaapi.html for more details.
NB: only set the -y parameter (word-sense disambiguation) if wsdserverctl is running.
7. outputASName: output Annotation Set name.
8. outputASType: output annotation name to be used for all MetaMap annotations
9. outputMode: determines which mappings are output as annotations in the GATE
document, for each phrase:
• AllCandidatesAndMappings: annotate both Candidate and final mappings.
This will usually result in multiple, overlapping annotations for each term/phrase
• AllMappings: annotate all the final MetaMap Mappings for each phrase. This
will result in fewer annotations with higher precision (e.g. for “lung cancer” only
the complete phrase will be annotated as Neoplastic Process [neop])
• HighestMappingOnly: annotate only the highest scoring MetaMap Mapping
for each phrase. If two Mappings have the same score, the first returned by
MetaMap is output.
• AllCandidates: annotate all Candidate mappings and not the final Mappings.
This will result in more annotations with less precision (e.g. for “lung cancer”
both “lung” (bpoc) and “lung cancer” (neop) will be annotated).
10. taggerMode: determines whether all term instances are processed by MetaMap, the
first instance only, or the first instance with coreference annotations added. Only used
if the inputASTypes parameter has been set.
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• FirstOccurrenceOnly: only process and annotate the first instance of each term
in the document
• CoReference: process and annotate the first instance and coreference following
instances
• AllOccurrences: process and annotate all term instances independently
20.27.2
Upgrading from an earlier version of the plugin
The Tagger MetaMap plugin was completely rewritten for GATE 6.1, and the current version
is not parameter-compatible with the version provided in GATE 6.0 and earlier. The previous
version of the plugin is still available under the Obsolete plugins directory, but we recommend
you upgrade your application to use the new plugin version.
Specifically, the following parameters have been removed: mmServerHost, mmServerPort,
mmServerTimeout, excludeSemanticTypes, restrictSemanticTypes, scoreThreshold.
These can now be specified using the --metamap server host, --metamap server port,
--metamap server timeout, -k, -J and -r options in the metaMapOptions run-time parameter string.
• outputASType has been made a run-time parameter.
• useNegEx has been renamed to annotateNegEx
The following changes have been made to outputMode
• MappingsOnly has been renamed to AllMappings
• CandidatesOnly has been renamed to AllCandidates
• CandidatesAndMappings has been renamed to AllCandidatesAndMappings
See the previous section for details of the additional parameters that have been added in the
new version of the plugin.
20.28
Content Detection Using Boilerpipe
When working in a closed domain it is often possible to craft a few JAPE rules to separate
real document content from the boilerplate headers, footers, menus, etc. that often appear,
especially when dealing with web documents. As the number of document sources increases,
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however, it becomes difficult to separate content from boilerplate using hand crafted rules
and a more general approach is required.
The ‘Tagger Boilerpipe’ plugin contains a PR that can be used to apply the boilerpipe
library (see http://code.google.com/p/boilerpipe/) to GATE documents in order to annotate the content sections. The boilerpipe library is based upon work reported in
[Kohlschütter et al. 10], although it has seen a number of improvements since then. Due
to the way in which the library works not all features are currently available through the
GATE PR.
The PR is configured using the following runtime parameters:
• allContent: this parameter defines how the mime type parameter should be interpreted and if documents should, instead of being processed, by assumed to contain
nothing but actual content. defaults to ‘If Mime Type is NOT Listed’ which means
that any document with a mime type not listed is assumed to be all content.
• annotateBoilerplate: should we annotate the boilerplate sections of the document,
defaults to false.
• annotateContent: should we annotate the main content of the document, defaults
to true.
• boilerplateAnnotationName: the name of the annotation type to annotate sections determined to be boilerplate, defaults to ‘Boilerplate’. Whilst this parameter is
optional is must be specified if annotateBoilerplate is set to true.
• contentAnnotationName: the name of the annotation type to annotate sections
determined to be content, defaults to ‘Content’. Whilst this parameter is optional is
must be specified if annotateContent is set to true.
• debug: if true then annotations created by the PR will contain debugging info, defaults
to false.
• extractor: specifies the boilerpipe extractor to use, defaults to the default extractor.
• failOnMissingInputAnnotations: if the input annotations (Tokens) are missing
should this PR fail or just not do anything, defaults to true to allow obvious mistakes
in pipeline configuration to be captured at an early stage.
• inputASName: the name of the input annotation set
• mimeTypes: a set of mime types that control document processing, defaults to text/html. The exact behaviour of the PR is dependent upon both this parameter and
the value of the allContent parameter.
• ouputASName: the name of the output annotation set
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More (CREOLE) Plugins
• useHintsFromOriginalMarkups: often the original markups will provide hints that
may be useful for correctly identifying the main content of the document. If true, useful
markup (currently the title, body, and anchor tags) will be used by the PR to help
detect content, defaults to true.
20.29
Inter Annotator Agreement
The IAA plugin, “Inter Annotator Agreement”, computes interannotator agreement measures for various tasks. For named entity annotations, it computes the F-measures, namely
Precision, Recall and F1, for two or more annotation sets. For text classification tasks, it
computes Cohen’s kappa and some other IAA measures which are more suitable than the
F-measures for the task. This plugin is fully documented in Section 10.5. Chapter 10 introduces various measures of interannotator agreement and describes a range of tools provided
in GATE for calculating them.
20.30
Schema Annotation Editor
The plugin ‘Schema Annotation Editor’ constrains the annotation editor to permitted types.
See Section 3.4.6 for more information.
Part IV
The GATE Family: Cloud, MIMIR,
Teamware
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Chapter 21
GATE Cloud
The growth of unstructured content on the internet has resulted in an increased need for researchers in diverse fields to run language processing and text mining on large-scale datasets,
many of which are impossible to process in reasonable time on standard desktops. However,
in order to take advantage of the on-demand compute power and data storage on the cloud,
NLP researchers currently have to re-write/adapt their algorithms.
Therefore, we have now adapted the GATE infrastructure (and its JAPE rule-based and
machine learning engines) to the cloud and thus enabled researchers to run their GATE
applications without a significant overhead. In addition to lowering the barrier to entry,
GATE Cloud also reduces the time required to carry out large-scale NLP experiments by
allowing researchers to harness the on-demand compute power of the cloud.
Cloud computing means many things in many contexts. On GATECloud.net it means:
• zero fixed costs: you don’t buy software licences or server hardware, just pay for the
compute time that you use
• near zero startup time: in a matter of minutes you can specify, provision and deploy
the type of computation that used to take months of planning
• easy in, easy out: if you try it and don’t like it, go elsewhere! you can even take the
software with you, it’s all open source
• someone else takes the admin load: - the GATE team from the University of
Sheffield make sure you’re running the best of breed technology for text, search and
semantics
• cloud providers’ data center managers (we use Amazon Inc.) make sure the hardware
and operating platform for your work is scaleable, reliable and cheap
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GATE Cloud
GATE is (and always will be) free, but machine time, training, dedicated support and
bespoke development is not. Using GATECloud you can rent cloud time to process large
batches of documents on vast server farms, or academic clusters. You can push a terabyte
of annotated data into an index server and replicate the data across the world. Or just
purchase training services and support for the various tools in the GATE family.
21.1
GATE Cloud services: an overview
At the time of writing, there are several kinds of services, offerred from GATE Cloud, but
they will be growing significantly over the course of the next six months, so for an up-to-date
list see https://gatecloud.net/shopfront.
• GATE Annotation Services: these allow you to run a GATE application, on the
cloud, over large document collections. The GATE application can be created by the
user in GATE Developer and uploaded on the cloud, or users can use some pre-packaged
applications, e.g., ANNIE, ANNIE with Number and Measurement add-ons.
• GATE Teamware (Chapter 22): a web-based collaborative annotation tool, that supports distributed teams of manual annotators and data managers/curators to produce
gold-standard corpora for evaluation and training.
• GATE MIMIR (Chapter 23): a multi-paradigm information management index and
repository which can be used to index and search over text, annotations, semantic
schemas (ontologies), and semantic meta-data (instance data). It allows queries that
arbitrarily mix full- text, structural, linguistic and semantic queries and that can scale
to terabytes of text.
21.2
Comparison with other systems
There are several other text-analysis-as-a-service systems out there that do some of what we
do. Here are some differences:
• We’re the only open source solution.
• We’re the only customisable solution we support a bring-your-own-annotator option
– a GATE pipeline – as well as pre-packaged entity annotation services like other
systems.
• We’re the only end-to-end full lifecycle solution. We don’t just do entity extraction
– we do data preparation, inter-annotator agreement, quality assurance and control,
data visualisation, indexing and search of full text/annotation graph/ontology/instance
store, etc. etc. etc.
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• Bulk upload of documents to process, no need to use programming APIs.
• No recurring monthly costs, pay-per-use, billed per hour.
• No daily limit on number of documents to process.
• No limit on document size.
• Costs of processing dependent on overall data size, not number of documents.
• Web-based collaborative annotation tool to correct mistakes and create training
and evaluation data (see Chapter 22).
• Speed: other systems price per document (we price on processing time) – this makes
it impossible to compare like with like (do you really want to compare the processing
of individual tweets against 200 page technical reports?!). GATECloud is also heavily
optimised for high volumes – if you want to do low volumes, you can do them on
your netbook.
• Community: we’ve been here for more than 15 years, and our community of developers, users, third party suppliers and so on is second to none.
21.3
How to buy services
Before you can buy any of our cloud based offerings you need to create an account on
GATECloud.net, use the Register link at the top right of any page and follow the instructions.
Once registered and logged in you can browse through the shop and decided on the services
you wish to purchase.
The shop does not handle money but works instead with vouchers bought from the University
of Sheffield’s on-line shop. Vouchers are available in multiples of £5, the amount you need to
purchase will depend upon the services you wish to use. Once you are ready to buy time on
GATECloud.net create an account with the University shop and then buy the appropriate
amount credit vouchers. Be sure to use the same email address when buying vouchers as
when registering for a GATECloud.net account so that credit you purchase can automatically
be added to your GATECloud.net account.
Once you have enough credit you can click through to the checkout where you can review
your basket before finalizing your order.
Annotation job purchases should appear instantly within your dashboard. Teamware servers
take a little longer to create and we will e-mail you when the server is ready for use.
All past purchases can be monitored and controlled via your dashboard.
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GATE Cloud
Figure 21.1: Shopping stages
21.4
Pricing and discounts
We run your jobs in the cloud and we pass on the cloud costs, plus a small premium. We do
not have our own private cloud, so each job we run costs us money. Therefore we can’t run
a zero cost service, but we do supply discounts and freebies for people wanting to try the
service. To get a discount:
• create an account
• use the GATE Cloud contact page to send us your user name and request for discount
• we apply a pricing rule to your account
• you then shop in the normal manner, as described in Section 21.3 above.
A last word on pricing: the underlying software is all open source, so there’s nothing to stop
you rolling your own if you can’t afford the cloud costs.
GATE Cloud
21.5
503
Annotation Jobs on GATECloud.net
GATECloud.net annotation jobs provide a way to quickly process large numbers of documents using a GATE application, with the results exported to files in GATE XML or XCES
format and/or sent to a Mimir server for indexing. Annotation jobs are optimized for the
processing of large batches of documents (tens of thousands or more) rather than processing
a small number of documents on the fly (GATE Developer is best suited for the latter).
To submit an annotation job you first choose which GATE application you want to run.
GATECloud.net provides some standard pre-packaged applications (e.g., ANNIE), or you
can provide your own application (see Section 21.6). You then upload the documents you
wish to process packaged up into ZIP or (optionally compressed) TAR archives, or ARC
files (as produced by the Heritrix web crawler), and decide which annotations you would like
returned as output, and in what format.
When the job is started, GATECloud.net takes the document archives you provided and
divides them up into manageable-sized batches of up to 15,000 documents. Each batch is
then processed using the GATE paralleliser and the generated output files are packaged up
and made available for you to download from the GATECloud.net site when the job has
completed.
21.5.1
The Annotation Service Charges Explained
GATECloud.net annotation jobs run on a public commercial cloud, which charges us per
hour for the processing time we consume. As GATECloud.net allows you to run your own
GATE application, and different GATE applications can process radically different numbers
of documents in a given amount of time (depending on the complexity of the application)
we cannot adopt the ”£x per thousand documents” pricing structure used by other similar
services. Instead, GATECloud.net passes on to you, the user, the per-hour charges we pay
to the cloud provider plus a small mark-up to cover our own costs.
For a given annotation job, we add up the total amount of compute time taken to process
all the individual batches of documents that make up your job (counted in seconds), round
this number up to the next full hour and multiply this by the hourly price for the particular
job type to get the total cost of the job. For example, if your annotation job was priced at
£1 per hour and split into three batches that each took 56 minutes of compute time then
the total cost of the job would be £3 (178 minutes of compute time, rounded up to 3 hours).
However, if each batch took 62 minutes to process then the total cost would be £4 (184
minutes, rounded up to 4 hours).
While the job is running, we apply charges to your account whenever a job has consumed
ten CPU hours since the last charge (which takes considerably less than ten real hours as
several batches will typically execute in parallel). If your GATECloud.net account runs out
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of funds at any time, all your currently-executing annotation jobs will be suspended. You
will be able to resume the suspended jobs once you have topped up your account to clear the
negative balance. Note that it is not possible to download the result files from completed
jobs if your GATECloud.net account is overdrawn.
21.5.2
Annotation Job Execution in Detail
Each annotation job on GATECloud.net consists of a number of individual tasks:
• First a single ”split” task which takes the initial document archives that were provided
when the job was configured and splits them into manageable batches for processing.
– ARC files are not currently split - each complete ARC file will be processed as a
single processing task.
– ZIP files that are smaller than 50MB will not be split, and will be processed as a
single processing task.
– ZIP files larger than 50MB, and all TAR files, will be split into chunks of maximum
size 50MB (compressed size) or 15,000 documents, whichever is the smaller. Each
chunk will be processed as a separate processing task.
• One or more processing tasks, as determined by the split task described above. Each
processing task will run the GATE application over the documents from its input chunk
as defined by the input specification, and save any output files in ZIP archives of no
more than 100MB, which will be available to download once the job is complete.
• A final ”join” task to collate the execution logs from the processing tasks and produce
an overall summary report.
Note that because ZIP and TAR input files may be split into chunks, it is important that
each input document in the archive should be self-contained, for example XML files should
not refer to a DTD stored elsewhere in the ZIP file. If your documents do have external
dependencies such as DTDs then you have two choices, you can either (a) use GATE Developer to load your original documents and re-save them as GATE XML format (which is self
contained), or (b) use a custom annotation job (see below) and include the additional files
in your application ZIP, and refer to them using absolute paths.
21.6
Running Custom Annotation Jobs on GATECloud.net
GATECloud.net provides a way for you to run pretty much any GATE application on the
cloud. You develop your application in the usual way using GATE Developer and then save
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it as a single self-contained ZIP file, typically using the ”Export for GATECloud.net” option.
This section tells you what you need to know to ensure that your application will run on
GATECloud.net.
21.6.1
Preparing Your Application: The Basics
You supply your GATE application to GATECloud.net as a single ZIP file, which is expected
to contain a saved application state in the usual ”.xgapp” format, along with all the GATE
plugins, JAPE grammars and other resources that the application requires. The saved application state must be named application.xgapp and must be located at the ’root directory’
of the zip file (i.e. when the ZIP is unpacked it must leave a file named application.xgapp
in the directory where the ZIP is unpacked and not in a sub-directory). All URL paths used
by the application should be relative paths that do not contain any ’..’ components, so they
will point to files in the same directory as application.xgapp or a sub-directory under this
location.
Figure 21.2: Application ZIP structure
The easiest way to build such a package is simply to save your application in GATE Developer
using the ”Export for GATECloud.net” option, which produces a ZIP file containing an
application.xgapp and all its required resources in one click.
21.6.2
The GATECloud.net environment
For many GATE applications that just use the standard pure-Java ANNIE components, the
basic information above is all you need to know to run your application on GATECloud.net.
But for more advanced applications that involve custom PRs, platform-specific native helpers
(such as an external tagger), or other components that need to know the path where they
are installed, you will need to know a little more about the environment in which your
application will be running.
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Hardware and software
GATECloud.net annotation jobs are executed on virtual 64-bit (x86 64) Linux servers in the
cloud, specifically Ubuntu 10.10 (Maverick Meerkat). The GATE application is run using
the open-source GCP tool1 on Sun Java 6 (1.6.0 21). The current offering uses the Amazon
EC2 cloud, and runs jobs on their ’m1.xlarge’ machines which provide 4 virtual CPU cores
and 15GB of memory, of which 13GB is available to the GCP process.
The GCP (GATE Cloud Paralleliser) process is configured for ’headless’ operation
(-Djava.awt.headless=true), and your code should not assume that a GUI display is
available.
GCP loads one copy of your application.xgapp in the usual way using the PersistenceManager. It then uses the GATE duplication mechanism to make a further 5 independent copies of the loaded application, and runs 6 parallel threads to process your documents. For most PRs this duplication process is essentially equivalent to loading the original
application.xgapp 6 times but if you are writing a custom PR you may wish to consider
implementing a custom duplication strategy.
Directories
The application ZIP file will always be unpacked in a directory named /gatecloud/application
on the cloud server. Thus the application file will always be
/gatecloud/application/application.xgapp and if any of your components need to
know the absolute path to their resource files you can work this out by prepending /gatecloud/application/ to the path of the entry inside your ZIP package.
The user account that runs the GCP process has full read and write access in the
/gatecloud/application directory, so if any of your components need to create temporary
files then this is a good place to put them. Any files created under /gatecloud/application
will be lost when the current batch of documents has been processed.
The directory /gatecloud/batch/output is where GCP will write any output files specified
by the output definitions you supply when running an annotation job. All files created
under this directory will be packaged up into ZIP files when the batch of documents has
been processed and made available for download when the job has completed. Thus, any
additional output files that your application creates and that need to be returned to the user
should be placed under /gatecloud/batch/output.
Your code should not assume it has permission to read and write any files outside these two
locations.
1
Source code is available in the subversion repository at
https://gate.svn.sourceforge.net/svnroot/gate/gcp/trunk
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Native code components
Many PRs are simply wrappers around non-Java tools, for example third-party taggers of
various kinds. If your application requires the use of any non-Java components you must
ensure that the version you include in your ZIP package is the one that will run on Linux
x86 64, and in particular on Ubuntu 10.10. The cloud processing servers have a reasonable
set of packages installed by default, including a basic install of Perl and Python, sed, awk
and bash. To request additional packages please contact GATE Cloud support with your
requirements. If you want to be sure your code will work on GATECloud.net then the
best approach is to sign up for your own account at Amazon Web Services, run your own
instance of the same machine image that GATECloud.net uses and test the software yourself.
As Amazon charges by the hour with no up-front fees this should cost you very little.
As your code will be running in a Linux environment, remember that any native executable
or script that your application needs to call must be marked with execute permission on
the filesystem. GATECloud.net uses the standard Info-ZIP ”unzip” tool to unpack the
application ZIP package, which respects permission settings specified in the ZIP file, so if
you build your package using the corresponding ”zip” tool the permissions will be preserved.
However, many ZIP file creation tools (including GATE’s ”Export for GATECloud.net”) do
not preserve permissions in this way. Therefore GATECloud.net also supports an alternative
mechanism to mark files as executable.
Once the application ZIP has been unpacked, we look through the resulting directory tree
for files named .executables. If any such file is found, we treat each line in the file as
a relative path, and set the execute flag on the corresponding file in the file system. For
example, imagine the following structure:
application.xgapp
plugins
- MyTagger
- resources
- tagger.sh
- postprocessor.pl
Here, tagger.sh and postprocessor.pl are scripts that need to be marked as executable, so we
could create a file plugins/MyTagger/.executables containing the two lines:
resources/tagger.sh
resources/postprocessor.pl
or equivalently, create plugins/MyTagger/resources/.executables containing
tagger.sh
postprocessor.pl
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Either way, the effect would be to make the GATECloud.net processing machine mark the
relevant files as executable before running your application.
Security and privacy
GATECloud.net does not run a separate machine for each annotation job. Instead it splits
each annotation job up into manageable pieces (referred to as tasks), puts these tasks into a
queue, and runs a collection of processing machines (referred to as ”nodes”) that simply take
the next task from the queue whenever they have finished processing their previous task.
While a task is running it has exclusive use of that particular node - we never run more than
one task on the same node at the same time - but once the task is complete the same node
will then run another task (which may or may not be part of the same annotation job).
To ensure the security and privacy of your code and data, the node takes the following
precautions:
• All GCP processes are run as an unprivileged user account which only has write permission in a restricted area of the filesystem (see above).
• At the end of every task, all processes running under that user ID are forcibly terminated (so there’s no risk of a stray or malicious background process started by a
previous task being able to read your data).
• The /gatecloud/application and /gatecloud/batch directories are completely
deleted at the end of every task (whether the task completed successfully or failed)
so your data will not be left for the following task to see.
Chapter 22
GATE Teamware: A Web-based
Collaborative Corpus Annotation Tool
Current tools demonstrate that text annotation projects can be approached successfully in
a collaborative fashion. However, we believe that this can be improved further by providing
a unified environment that provides a multi-role methodological framework to support the
different phases and actors in the annotation process. The multi-role support is particularly
important, as it enables the most efficient use of the skills of the different people and lowers
overall annotation costs through having simple and efficient annotation web-based UIs for
non-specialist annotators. In this paper we present Teamware, a novel web-based collaborative annotation environment which enables users to carry out complex corpus annotation
projects, involving less skilled, cheaper annotators working remotely from within their web
browsers. It has been evaluated by us through the creation of several gold standard corpora,
as well as through external evaluation in commercial annotation projects.
For technical and user interface details not covered in this chapter, please refer to the
Teamware User Guide.
GATE Teamware is open-source software, released under the GNU Affero General Public
Licence version 3. Commercial licences are available from the University of Sheffield. The
source code is available from the subversion repository at
https://gate.svn.sourceforge.net/svnroot/gate/teamware/trunk
22.1
Introduction
For the past ten years, NLP development frameworks such as OpenNLP, GATE, and UIMA
have been providing tool support and facilitating NLP researchers with the task of implementing new algorithms, sharing, and reusing them. At the same time, Information
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Extraction (IE) research and computational linguistics in general has been driven forward
by the growing volume of annotated corpora, produced by research projects and through
evaluation initiatives such as MUC [Marsh & Perzanowski 98], ACE1 , DUC [DUC 01], and
CoNLL shared tasks. Some of the NLP frameworks (e.g., AGTK [Maeda & Strassel 04],
GATE [Cunningham et al. 02]) even provide text annotation user interfaces. However, much
more is needed in order to produce high quality annotated corpora: a stringent methodology,
annotation guidelines, inter-annotator agreement measures, and in some cases, annotation
adjudication (or data curation) to reconcile differences between annotators.
Current tools demonstrate that annotation projects can be approached in a collaborative
fashion successfully. However, we believe that this can be improved further by providing
a unified environment that provides a multi-role methodological framework to support the
different phases and actors in the annotation process. The multi-role support is particularly
important, as it enables the most efficient use of the skills of the different people and lowers
overall annotation costs through having simple and efficient annotation web-based UIs for
non-specialist annotators. This also enables role-based security, project management and
performance measurement of annotators, which are all particularly important in corporate
environments.
This chapter presents Teamware, a web-based software suite and a methodology for the
implementation and support of complex annotation projects. In addition to its research
uses, it has also been tested as a framework for cost-effective commercial annotation services,
supplied either as in-house units or as outsourced specialist activities.
In comparison to previous work Teamware is a novel general purpose, web-based annotation
framework, which:
• structures the roles of the different actors involved in large-scale corpus annotation
(e.g., annotators, editors, managers) and supports their interactions in an unified environment;
• provides a set of general purpose text annotation tools, tailored to the different user
roles, e.g., a curator management tool with inter-annotator agreement metrics and
adjudication facilities and a web-based document tool for in-experienced annotators;
• supports complex annotation workflows and provides a management console with business process statistics, such as time spent per document by each of its annotators,
percentage of completed documents, etc;
• offers methodological support, to complement the diverse technological tool support.
1
http://www.ldc.upenn.edu/Projects/ACE/
GATE Teamware: Web-based Annotation Tool
22.2
511
Requirements for Multi-Role Collaborative Annotation Environments
As discussed above, collaborative corpus annotation is a complex process, which involves
different kinds of actors (e.g., annotators, editors, managers) and also requires a diverse
range of pre-processing, a user interface, and evaluation tools. Here we structure all these
into a coherent set of key requirements, which arise from our goal to provide cost-effective
corpus annotation.
Firstly, due to the multiple actors involved and their complex interactions, a collaborative environment needs to support these different roles through user groups, access privileges,
and corresponding user interfaces. Secondly, since many annotation projects manipulate
hundreds of documents, there needs to be a remote, efficient data storage. Thirdly, significant cost savings can be achieved through pre-annotating corpora automatically, which
in turns requires support for automatic annotation services and their flexible configuration. Last, but not least, a flexible workflow engine is required to capture the complex
requirements and interactions.
Next we discuss the four high-level requirements in finer-grained details.
22.2.1
Typical Division of Labour
Due to annotation projects having different sizes and complexity, in some cases the same
person might perform more than one role or new roles might be needed. For example, in
small projects it is common that the person who defines and manages the project is also the
one who carries out quality assurance and adjudication. Nevertheless these are two distinct
roles (manager vs editor), involving different tasks and requiring different tool support.
Annotators are given a set of annotation guidelines and often work on the same document
independently. This is needed in order to get more reliable results and/or measure how well
humans perform the annotation task (see more on Inter-Annotator Agreement (IAA) below).
Consequently, manual annotation is a slow and error-prone task, which makes overall corpus
production very expensive. In order to allow the involvement of less-specialised annotators,
the manual annotation user interface needs to be simple to learn and use. In addition, there
needs to be an automatic training mode for annotators where their performance is compared
against a known gold standard and all mistakes are identified and explained to the annotator,
until they have mastered the guidelines.
Since the annotators and the corpus editors are most likely working at different locations,
there needs to be a communication channel between them, e.g., instant messaging. If an
editor/manager is not available, an annotator should also be able to mark an annotation
as requiring discussion and then all such annotations should be shown automatically in
the editor console. In addition, the annotation environment needs to restrict annotators
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to working on a maximum of n documents (given as a number or percentage), in order to
prevent an over-zealous annotator from taking over a project and introducing individual
bias. Annotators also need to be able to save their work and, if they close the annotation
tool, the same document must be presented to them for completion the next time they log
in.
From the user interface perspective, there needs to be support for annotating document-level
metadata (e.g., language identification), word-level annotations (e.g., named entities, POS
tags), and relations and trees (e.g., co-reference, syntax trees). Ideally, the interface should
offer some generic components for all these, which can be customised with the project-specific
tags and values via an XML schema or other similar declarative mechanism. The UI also
needs to be extensible, so specialised UIs can easily be plugged in, if required.
Editors or curators are responsible for measuring Inter-Annotator Agreement (IAA), annotation adjudication, gold-standard production, and annotator training. They also need
to communicate with annotators when questions arise. Therefore, they need to have wider
privileges in the system. In addition to the standard annotation interfaces, they need to
have access to the actual corpus and its documents and run IAA metrics. They also need
a specialised adjudication interface which helps them identify and reconcile differences in
multiply annotated documents. For some annotation projects, they also need to be able to
send a problematic document back for re-annotation.
Project managers are typically in charge of defining new corpus annotation projects and
their workflows, monitoring their progress, and dealing with performance issues. Depending
on project specifics, they may work together with the curators and define the annotation
guidelines, the associated schemas (or set of tags), and prepare and upload the corpus to
be annotated. They also make methodological choices: whether to have multiple annotators
per document; how many; which automatic NLP services need to be used to pre-process the
data; and what is the overall workflow of annotation, quality assurance, adjudication, and
corpus delivery.
Managers need a project monitoring tool where they can see:
• Whether a corpus is currently assigned to a project or, what annotation projects have
been run on the corpus with links to these projects or their archive reports (if no longer
active). Also links to the the annotation schemas for all annotation types currently in
the corpus.
• Project completion status (e.g., 80% manually annotated, 20% adjudicated).
• Annotator statistics within and across projects: which annotator worked on each of
the documents, what schemas they used, how long they took, and what was their IAA
(if measured).
• The ability to lock a corpus from further editing, either during or after a project.
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• Ability to archive project reports, so projects can be deleted from the active list.
Archives should preserve information on what was done and by whom, how long it
took, etc.
22.2.2
Remote, Scalable Data Storage
Given the multiple user roles and the fact that several annotation projects may need to be
running at the same time, possibly involving different, remotely located teams, the data
storage layer needs to scale to accommodate large, distributed corpora and have the necessary security in place through authentication and fine-grained user/group access control.
Data security is paramount and needs to be enforced as data is being sent over the web
to the remote annotators. Support for diverse document input and output formats is also
necessary, especially stand-off ones when it is not possible to modify the original content.
Since multiple users can be working concurrently on the same document, there needs to be
an appropriate locking mechanism to support that. The data storage layer also needs to
provide facilities for storing annotation guidelines, annotation schemas, and, if applicable,
ontologies. Last, but not least, a corpus search functionality is often required, at least one
based on traditional keyword-based search, but ideally also including document metadata
and linguistic annotations.
22.2.3
Automatic annotation services
Automatic annotation services can reduce significantly annotation costs (e.g., annotation of
named entities), but unfortunately they also tend to be domain or application specific. Also,
several might be needed in order to bootstrap all types that need to be annotated, e.g.,
named entities, co-reference, and relation annotation modules. Therefore, the architecture
needs to be open so that new services can be added easily. Such services can encapsulate
different IE modules and take as input one or more documents (or an entire corpus). The
automatic services also need to be scalable, in order to minimise their impact on the overall
project completion time. The project manager should also be able to choose services based
on their accuracy on a given corpus.
Machine Learning (ML) IE modules can be regarded as a specific kind of automatic service.
A mixed initiative system [Day et al. 97] can be set up by the project manager and used to
facilitate manual annotation behind the scenes. This means that once a document has been
annotated manually, it will be sent to train the ML service which internally generates an
ML model. This model will then be applied by the service on any new document, so that
this document will be partially pre-annotated. The human annotator then only needs to
validate or correct the annotations provided by the ML system, which makes the annotation
task significantly faster [Day et al. 97].
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22.2.4
GATE Teamware: Web-based Annotation Tool
Workflow Support
In order to have an open, flexible model of corpus annotation processes, we need a powerful
workflow engine which supports asynchronous execution and arbitrary mix of automatic and
manual steps. For example, manual annotation and adjudication tasks are asynchronous.
Resilience to failures is essential and workflows need to save intermediary results from time to
time, especially after operations that are very expensive to re-run (e.g. manual annotation,
adjudication). The workflow engine also needs to have status persistence, action logging,
and activity monitoring, which is the basis for the project monitoring tools.
In a workflow it should be possible for more than one annotator to work on the same
document at the same time, however, during adjudication by editors, all affected annotations
need to be locked to prevent concurrent modifications. For separation of concerns, it is also
often useful if the same corpus can have more than one active project. Similarly, the same
annotator needs to be able to work on several annotation projects.
Figure 22.1: Teamware Architecture Diagram
22.3
Teamware:
Examples
Architecture, Implementation, and
Teamware is a web-based collaborative annotation and curation environment, which allows
unskilled annotators to be trained and then used to lower the cost of corpus annotation
projects. Further cost reductions are achieved by bootstrapping with relevant automatic
annotation services, where these exist, and/or through mixed initiative learning methods. It
has a service-based architecture which is parallel, distributed, and also scalable (via service
replication) (see Figure 22.1).
As shown in Figure 22.1, the Teamware architecture consists of SOAP web services for
data storage, a set of web-based user interfaces (UI Layer), and an executive layer in the
middle where the workflows of the specific annotation projects are defined. The UI Layer
is connected with the Executive Layer for exchanging command and control messages (such
as requesting the ID for document that needs to be annotated next), and also it connects
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Figure 22.2: Dynamic Workflow Configuration: Example
directly to the services layer for data-intensive communication (such as downloading the
actual document data, and uploading back the annotations produced).
22.3.1
Data Storage Service
The storage service provides a distributed data store for corpora, documents, and annotation
schemas. Input documents can be in all major formats (e.g. plain text, XML, HTML, PDF),
based on GATE’s comprehensive support. In all cases, when a document is created/imported
in Teamware, the format is analysed and converted into GATE’s single unified, graph-based
model of annotation. Then this internal annotation format is used for data exchange between
the service layer, the executive layer and the UI layer. Different processes within Teamware
can add and remove annotation data within the same document concurrently, as long as two
processes do not attempt to manipulate the same subset of the data at the same time. A
locking mechanism is used to ensure this and prevent data corruption. The main export format for annotations is currently stand-off XML, including XCES [Ide et al. 00]. Document
text is represented internally using Unicode and data exchange uses the UTF-8 character
encoding, so Teamware supports documents written in any natural language supported by
the Unicode standard (and the Java platform).
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Figure 22.3: Dynamic Workflow Configuration: Example
22.3.2
Annotation Services
The Annotation Services (GAS) provide distribution of compute-intensive NLP tasks over
multiple processors. It is transparent to the external user how many machines are actually
used to execute a particular service. GAS provides a straightforward mechanism for running
applications, created with the GATE framework, as web services that carry out various NLP
tasks. In practical applications we have tested a wide range of services such as named entity
recognition (based on the freely-available ANNIE system [Cunningham et al. 02]), ontology population [Maynard et al. 09], patent processing [Agatonovic et al. 08], and automatic
adjudication of multiple annotation layers in corpora.
The GAS architecture is itself layered, with a separation between the web service endpoint
that accepts requests from clients and queues them for processing, and one or more workers
that take the queued requests and process them. The queueing mechanism used to communicate between the two sides is the Java Messaging System (JMS)2 , a standard framework
for reliable messaging between Java components, and the configuration and wiring together
of all the components is handled using the Spring Framework 3 .
The endpoint, message queue and worker(s) are conceptually and logically separate, and
2
3
http://java.sun.com/products/jms/
http://www.springsource.org/
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may be physically hosted within the same Java Virtual Machine (VM), within separate VMs
on the same physical host, or on separate hosts connected over a network. When a service
is first deployed it will typically be as a single worker which resides in the same VM as
the service endpoint. This may be adequate for simple or lightly-loaded services but for
more heavily-loaded services additional workers may be added dynamically without shutting
down the web service, and similarly workers may be removed when no longer required. All
workers that are configured to consume jobs from the same endpoint will transparently share
the load. Multiple workers also provide fault-tolerance – if a worker fails its in-progress jobs
will be returned to the queue and will be picked up and handled by other workers.
22.3.3
The Executive Layer
Firstly, the executive layer implements authentication and user management, including role
definition and assignment. In addition, administrators can define here which UI components
are made accessible to which user roles (the defaults are shown in Figure 22.1).
The second major part is the workflow manager, which is based on JBoss jBPM4 and has
been developed to meet most of the requirements discussed in Section 22.2.4 above. Firstly,
it provides dynamic workflow management: create, read, update, delete (CRUD) workflow
definitions, and workflow actions. Secondly, it supports business process monitoring, i.e.,
measures how long annotators take, how good they are at annotating, as well as reporting
the overall progress and costs. Thirdly, there is a workflow execution engine which runs the
actual annotation projects. As part of the execution process, the project manager selects
the number of annotators per document; the annotation schemas; the set of annotators and
curator(s) involved in the project; and the corpus to be annotated.
Figure 22.3 shows an example workflow template. The diagram on the right shows the choice
points in workflow templates - whether to do automatic annotation or manual or both; which
automatic annotation services to execute and in what sequence; and for manual annotation
– what schemas to use, how may annotators per document, whether they can reject annotating a document, etc. The left-hand side shows the actual selections made for this particular
workflow, i.e., use both automatic and manual annotation; annotate measurements, references, and sections; and have one annotator per document. Once this template is saved by
the project manager, then it can be executed by the workflow engine on a chosen corpus and
list of annotators and curators. The workflow engine will first call the automatic annotation
service to bootstrap and then its results will be corrected by human annotators.
The rationale behind having an executive layer rather than defining authentication and
workflow management as services similar to the storage and ontology ones comes from the
fact that Teamware services are all SOAP web services, whereas elements of the executive
layer are only in part implemented as SOAP services with the rest being browser based.
Conceptually also the workflow manager acts like a middleman that ties together all the
4
http://www.jboss.com/products/jbpm/
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Figure 22.4: The Schema-based Annotator UI
different services and communicates with the user interfaces.
22.3.4
The User Interfaces
The Teamware user interfaces are web-based and do not require prior installation. They
either rendered natively in the web browser or, for more complex UIs, a Java Web Start
wrapper is provided around some Swing-based GATE editors (e.g., the document editor and
the ANNIC viewer [Aswani et al. 05]). After the user logs in, the system checks their role(s)
and access privileges to determine which interface elements they are allowed to access.
Annotator User Interface
When manual annotators log into Teamware, they see a very simple web page with one link
to their user profile data and another one – to start annotating documents. The generic
schema-based annotator UI is shown in Figure 22.4 and it is a visual component in GATE,
which is reused here via Java Web Start5 . This removes the need to install GATE on
5
http://java.sun.com/javase/technologies/desktop/javawebstart/index.jsp
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Figure 22.5: Part of the Adjudicat